diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 0347f8bb..01d22f89 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -33,4 +33,4 @@ jobs:
       - name: Test with nbval
         shell: bash -l {0}
         run: |
-          python -m pytest --nbval-lax --nbval-cell-timeout=12000 --ignore=notebooks/Chinese-Growth.ipynb --ignore=notebooks/Harmenberg-Aggregation.ipynb notebooks/
+          python -m pytest --nbval-lax --nbval-cell-timeout=12000 --ignore=notebooks/Chinese-Growth.ipynb --ignore=notebooks/Harmenberg-Aggregation.ipynb --ignore=notebooks/The-Prescott-Real-Business-Cycle-RBC.ipynb notebooks/
diff --git a/binder/environment.yml b/binder/environment.yml
index 2f0e54d2..4a3277b2 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -8,6 +8,9 @@ dependencies:
   - linearmodels
   - tqdm
   - nbval
+  - matplotlib
+  - statsmodels
+  - scipy
   - pip
   - pip:
     - git+https://github.com/econ-ark/hark@master
diff --git a/markdown/DSGE-RA-K-Dynamics-Problems.md b/markdown/DSGE-RA-K-Dynamics-Problems.md
new file mode 100644
index 00000000..a2bf33e9
--- /dev/null
+++ b/markdown/DSGE-RA-K-Dynamics-Problems.md
@@ -0,0 +1,21 @@
+---
+name: DSGE-RA-K-Dynamics-Problems
+tags:
+  - DemARK
+  - Demonstration
+  - Teaching
+  - Notebook
+abstract: 'Capital dynamics exercises for a representative-agent stochastic growth model.'
+authors:
+  -
+    family-names: Carroll
+    given-names: "Christopher D."
+    orcid: "https://orcid.org/0000-0003-3732-9312"
+github_repo_url: https://github.com/econ-ark/DemARK
+notebooks:
+  -
+    notebooks/DSGE-RA-K-Dynamics-Problems.ipynb
+dashboards:
+---
+
+Capital dynamics exercises for a representative-agent stochastic growth model.
diff --git a/markdown/RamseyCassKoopmans.md b/markdown/RamseyCassKoopmans.md
new file mode 100644
index 00000000..b2d10a2b
--- /dev/null
+++ b/markdown/RamseyCassKoopmans.md
@@ -0,0 +1,24 @@
+---
+name: RamseyCassKoopmans
+tags:
+  - DemARK
+  - Demonstration
+  - Notebook
+abstract: 'Implements the Ramsey/Cass-Koopmans growth model using the time-elimination solution method.'
+authors:
+  -
+    family-names: Velasquez-Giraldo
+    given-names: "Mateo"
+    orcid: "https://orcid.org/0000-0001-7243-6776"
+  -
+    family-names: Carroll
+    given-names: "Christopher D."
+    orcid: "https://orcid.org/0000-0003-3732-9312"
+github_repo_url: https://github.com/econ-ark/DemARK
+notebooks:
+  -
+    notebooks/RamseyCassKoopmans.ipynb
+dashboards:
+---
+
+Numerical solution, phase diagram, and simulations for the Ramsey/Cass-Koopmans growth model.
diff --git a/markdown/The-Prescott-Real-Business-Cycle-RBC.md b/markdown/The-Prescott-Real-Business-Cycle-RBC.md
new file mode 100644
index 00000000..80b69217
--- /dev/null
+++ b/markdown/The-Prescott-Real-Business-Cycle-RBC.md
@@ -0,0 +1,20 @@
+---
+name: The-Prescott-Real-Business-Cycle-RBC
+tags:
+  - DemARK
+  - Demonstration
+  - Notebook
+abstract: 'Sets up, solves, and estimates the Prescott Real Business Cycle model.'
+authors:
+  -
+    family-names: Carroll
+    given-names: "Christopher D."
+    orcid: "https://orcid.org/0000-0003-3732-9312"
+github_repo_url: https://github.com/econ-ark/DemARK
+notebooks:
+  -
+    notebooks/The-Prescott-Real-Business-Cycle-RBC.ipynb
+dashboards:
+---
+
+Setup, solution, and estimation workflow for the Prescott Real Business Cycle model.
diff --git a/notebooks/DSGE-RA-K-Dynamics-Problems.ipynb b/notebooks/DSGE-RA-K-Dynamics-Problems.ipynb
new file mode 100644
index 00000000..afae2d98
--- /dev/null
+++ b/notebooks/DSGE-RA-K-Dynamics-Problems.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Capital Dynamics in a Stochastic Growth Model\n",
+    "\n",
+    "[![badge](https://img.shields.io/badge/Launch%20using%20-Econ--ARK-blue)](https://econ-ark.org/materials/dsge-ra-k-dynamics-problems#launch)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The handout [BrockMirman](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/DSGEmodels/BrockMirman) derived   some facts about a special case of a representative agent DSGE model where analytical results can be obtained.  This exercise asks you to explore this model and closely related ones further, numerically, by adapting tools from the [QuantEcon](https://lectures.quantecon.org/py/) treatment of [optimal growth](https://lectures.quantecon.org/py/optgrowth.html) (you should download their Jupyter notebook to get a start)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PROBLEM\n",
+    "## Calculate theoretical variance of $k$\n",
+    "The handout shows that if the productivity shocks $\\phi_{t}$ are iid and have variance $\\sigma^{2}_{\\phi}$ then\n",
+    "\n",
+    "<!-- $\\newcommand{\\var}{\\text{var}}$ -->\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "k_{t+1} & = & \\log \\alpha \\beta + \\alpha k_{t} + \\phi_{t}\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "Show that this implies that the variance of $k$ is \n",
+    "$$\n",
+    "\\begin{align}\n",
+    " \\var(k) & = & \\frac{\\sigma^{2}_{\\phi}}{1-\\alpha^{2}}\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PROBLEM\n",
+    "## Simulate the economy and calculate $\\var(k)$\n",
+    "\n",
+    "Now using the QuantEcon tools, simulate the economy under the baseline parameter values and show that your simulation results correspond to the theoretical result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PROBLEM\n",
+    "## Suppose $\\phi_{t}$ is serially correlated\n",
+    "\n",
+    "Now we want to consider a case where the level of productivity $\\epsilon$ is serially correlated:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\phi_{t} = \\zeta \\phi_{t-1} + \\nu_{t}\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "for some shock $\\nu$ with variance $\\sigma^{2}_{\\nu}$ and a serial correlation coefficient $0 < \\zeta < 1$.  Calculate the variance of $k$ under this new assumption."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "# PROBLEM\n",
+    "## Now Solve and Simulate the Model\n",
+    "\n",
+    "Use the tools provided on the QuantEcon website to solve the model.  Then, starting with a capital stock equal to the stochastic steady state, simulate the model for 100 periods five separate times.  Compare the variances you have calculated numerically to the formulas you derived analytically, and make some remarks about what this means for trying to calibrate the model to data by examining the variance of $k$ in empirical data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
+   "source": [
+    "## Now Do a Similar Exercise for the CRRA utility Model\n",
+    "\n",
+    "Use the QuantEcon code to solve the model for a value of relative risk aversion $\\rho = 3$.  Now calculate the variance of $k$ for this new model in the same way you did for the earlier model.  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "## Compare the Results to a linearized approximation\n",
+    "\n",
+    "Now numerically confirm the result from the BrockMirman handout that \n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "  y_{t+1} & = & \\alpha (y_{t} + \\log \\alpha \\beta ) + \\phi_{t+1}\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "Simulations go here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidden": true
+   },
+   "source": [
+    "To develop the rest of this problem, see the corresponding question DSGE-RA-K-Dynamics-Problems-and-Solutions.  In particular, need to plot the phase diagram and show how the consumption function changes with $\\rho$."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,py:percent",
+   "text_representation": {
+    "extension": ".py",
+    "format_name": "percent",
+    "format_version": "1.1",
+    "jupytext_version": "0.8.3"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/RamseyCassKoopmans.ipynb b/notebooks/RamseyCassKoopmans.ipynb
new file mode 100644
index 00000000..372c5d87
--- /dev/null
+++ b/notebooks/RamseyCassKoopmans.ipynb
@@ -0,0 +1,820 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numerical Solution of Ramsey/Cass-Koopmans model\n",
+    "\n",
+    "[![badge](https://img.shields.io/badge/Launch%20using%20-Econ--ARK-blue)](https://econ-ark.org/materials/ramseycasskoopmans#launch)\n",
+    "\n",
+    "\n",
+    "\n",
+    "## by [Mateo Velásquez-Giraldo](https://github.com/Mv77)\n",
+    "\n",
+    "This notebook implements a class representing Ramsey's growth model. Current capacities include:\n",
+    "- Numerically finding the consumption rule using the 'time elimination' method as originally implemented by Alexander Tabarrok and updated by Christopher D. Carroll in this [Wolfram Mathematica notebook](www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/Growth/RamseyNumericSolve.zip)\n",
+    "- Drawing the phase diagram of the model.\n",
+    "- Simulating optimal dynamics from a given starting point.\n",
+    "\n",
+    "A formal treatment of the exact version of the model implemented in this notebook can be found in [Christopher Carroll's graduate macroeconomics lecture notes](http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/Growth/RamseyCassKoopmans/).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Elimination\n",
+    "\n",
+    "The 'time elimination' method is a straightforward way to obtain the slope of the saddle path of the model (the 'consumption function').\n",
+    "\n",
+    "The idea is simple.  The model has two dynamic equations:\n",
+    "   * $dc/dt$ is given by the consumption Euler equation\n",
+    "   * $dk/dt$ is given by the dynamic budget constraint\n",
+    "\n",
+    "To produce a phase diagram $\\{k,c\\}$ space, though, we need $dc/dk$.  The 'time elimination' method is simply to note that \n",
+    "\n",
+    "$\\left(\\frac{dc/dt}{dk/dt}\\right)=\\left(\\frac{dc}{dk}\\right)$\n",
+    "\n",
+    "If we begin with some point that is on the saddle path (the consumption \"policy function\"), the equation for $dc/dk$ will tell us how to construct the function at other points along the saddle path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "code_folding": [
+     0
+    ],
+    "lines_to_next_cell": 1
+   },
+   "outputs": [],
+   "source": [
+    "# Setup\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from scipy.integrate import odeint\n",
+    "from scipy import interpolate\n",
+    "\n",
+    "from numpy import linalg as LA\n",
+    "\n",
+    "# This is a jupytext paired notebook \n",
+    "# which can be executed from a terminal command line via \"ipython [notebookname].py\"\n",
+    "# But a terminal does not permit inline figures, so we need to test jupyter vs terminal\n",
+    "# Google \"how can I check if code is executed in the ipython notebook\"\n",
+    "def in_ipynb():\n",
+    "    try:\n",
+    "        if str(type(get_ipython())) == \"<class 'ipykernel.zmqshell.ZMQInteractiveShell'>\":\n",
+    "            return True\n",
+    "        else:\n",
+    "            return False\n",
+    "    except NameError:\n",
+    "        return False\n",
+    "\n",
+    "# Determine whether to make the figures inline (for spyder or jupyter)\n",
+    "# vs whatever is the automatic setting that will apply if run from the terminal\n",
+    "if in_ipynb():\n",
+    "    # %matplotlib inline generates a syntax error when run from the shell\n",
+    "    # so do this instead\n",
+    "    get_ipython().run_line_magic('matplotlib', 'inline') \n",
+    "else:\n",
+    "    get_ipython().run_line_magic('matplotlib', 'auto') \n",
+    "\n",
+    "# Import the plot-figure library matplotlib\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['text.usetex'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Implement the RCKmod class\n",
+    "\n",
+    "class RCKmod:\n",
+    "    \"\"\"\n",
+    "    A class representing Ramsey/Cass-Koopmans growth models.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def __init__(self,rho,alpha,theta,xi,delta,phi):\n",
+    "        \"\"\"\n",
+    "        Inputs:\n",
+    "        - rho:   relative risk aversion coefficient for CRRA utility.\n",
+    "        - alpha: capital's share of production in Cobb-Douglas output function.\n",
+    "        - theta: time preference/discount rate.\n",
+    "        - xi:    population growth rate.\n",
+    "        - delta: capital depreciation rate.\n",
+    "        - phi:   labor productivity growth rate.        \n",
+    "        \"\"\"\n",
+    "        # Assign parameter values\n",
+    "        self.rho = rho\n",
+    "        self.alpha = alpha\n",
+    "        self.theta = theta\n",
+    "        self.xi = xi\n",
+    "        self.delta = delta\n",
+    "        self.phi = phi\n",
+    "        \n",
+    "        # Create empty consumption function\n",
+    "        self.cFunc = None\n",
+    "        \n",
+    "        # Maximum capital\n",
+    "        self.kmax = (1/(self.phi + self.xi + self.delta))**(1/(1-self.alpha))\n",
+    "        \n",
+    "        # Steady state capital\n",
+    "        self.kss = (alpha/(theta + xi + delta + rho*phi))**(1/(1-alpha))\n",
+    "        # Steady state consumption\n",
+    "        self.css = self.kss**alpha - (xi + delta + phi)*self.kss\n",
+    "        \n",
+    "        # Solve the model to create its consumption function\n",
+    "        self.solve()\n",
+    "    \n",
+    "    def output(self,k):\n",
+    "        \"\"\"\n",
+    "        Cobb-Douglas normalized production function\n",
+    "        \"\"\"\n",
+    "        return(k**self.alpha)\n",
+    "    \n",
+    "    def dcdt(self,c,k):\n",
+    "        \"\"\"\n",
+    "        Consumption differential equation\n",
+    "        \"\"\"\n",
+    "        dc = c/self.rho*(self.alpha*k**(self.alpha - 1) - self.theta -\\\n",
+    "                         (self.xi + self.delta) -self.rho*self.phi)\n",
+    "        return(dc)\n",
+    "    \n",
+    "    def dkdt(self,c,k):\n",
+    "        \"\"\"\n",
+    "        Capital differential equation\n",
+    "        \"\"\"\n",
+    "        dk = self.output(k) - c - (self.phi + self.xi + self.delta)*k\n",
+    "        return(dk)\n",
+    "        \n",
+    "    def dcdk(self,c,k):\n",
+    "        \"\"\"\n",
+    "        Differential equation for the time elimination method.\n",
+    "        This corresponds to dc/dk = (dc/dt)/(dk/dt)\n",
+    "        \"\"\"\n",
+    "        return(self.dcdt(c,k)/self.dkdt(c,k))\n",
+    "    \n",
+    "    def solve(self, eps = 10**(-8), npoints = 400):\n",
+    "        \"\"\"\n",
+    "        Solves for the model's consumption rule through the time elimination\n",
+    "        method.\n",
+    "        \n",
+    "        Parameters:\n",
+    "        - eps:     disturbance used to prevent dc/dk from becoming 0/0 at\n",
+    "                   the steady state value of capital.\n",
+    "        - npoints: number of points used on each side of the steady\n",
+    "                   state capital for solving the dc/dk equation.\n",
+    "        \"\"\"\n",
+    "        # K ranges, avoiding kss through a small disturbance\n",
+    "        k_below = np.linspace(self.kss-eps,0.0001,npoints)\n",
+    "        k_above = np.linspace(self.kss+eps,self.kmax,npoints)\n",
+    "        k = np.concatenate((k_below,k_above)).flatten()\n",
+    "        \n",
+    "        # Solve for c on each side of the steady state capital,\n",
+    "        # Using the slope of the saddle path to approximate initial\n",
+    "        # conditions\n",
+    "        c_below = odeint(self.dcdk,\n",
+    "                         self.css - eps*self.slope_ss(), k_below)\n",
+    "        c_above = odeint(self.dcdk,\n",
+    "                         self.css + eps*self.slope_ss(), k_above)\n",
+    "        \n",
+    "        c = np.concatenate((c_below,c_above)).flatten()\n",
+    "        \n",
+    "        # Create consumption function as an interpolation of the\n",
+    "        # numerical solutions.\n",
+    "        self.cFunc = interpolate.interp1d(k,c)\n",
+    "    \n",
+    "    \n",
+    "    def dkdt_opt(self,k,t):\n",
+    "        \"\"\"\n",
+    "        Differential equation for k assuming optimal c decisions.\n",
+    "        \"\"\"\n",
+    "        return(self.dkdt(self.cFunc(k),k))\n",
+    "    \n",
+    "    \n",
+    "    def k_dynamics(self,k0,t):\n",
+    "        \"\"\"\n",
+    "        Simulates optimal capital dynamics from a given starting point.\n",
+    "        Parameters:\n",
+    "        - t : vector of time points at which to solve for capital\n",
+    "        - k0: value of capital at t[0]\n",
+    "        \"\"\"\n",
+    "        k = odeint(self.dkdt_opt, k0, t)\n",
+    "        return(k)\n",
+    "    \n",
+    "    \n",
+    "    def k0locus(self,k):\n",
+    "        \"\"\"\n",
+    "        Returns the consumption value that leaves a given ammount of\n",
+    "        effective capital unchanged.\n",
+    "        \"\"\"\n",
+    "        return(self.output(k) - (self.phi + self.xi + self.delta)*k)\n",
+    "        \n",
+    "    def phase_diagram(self, npoints = 200, arrows = False, n_arrows = 5, labels = True, legend = True):\n",
+    "        \"\"\"\n",
+    "        Plots the model's phase diagram.\n",
+    "        - npoints:  number of ticks in the k axis.\n",
+    "        - arrows:   boolean to indicate whether or not to draw arrow\n",
+    "                    grid.\n",
+    "        - n_arrows: controls the number of arrows in the grid\n",
+    "        \"\"\"\n",
+    "        \n",
+    "        k = np.linspace(0.01,self.kmax,npoints)\n",
+    "        \n",
+    "        # Create plot\n",
+    "        plt.figure()\n",
+    "        \n",
+    "        # Plot k0 locus\n",
+    "        plt.plot(k,self.k0locus(k),label = '$\\\\dot{k}=0$ locus')\n",
+    "        # Plot c0 locus\n",
+    "        plt.axvline(x = self.kss,linestyle = '--',\n",
+    "                    label = '$\\\\dot{c}=0$ locus')\n",
+    "        # Plot saddle path\n",
+    "        plt.plot(k,self.cFunc(k), label = 'Saddle path')\n",
+    "        # Plot steady state\n",
+    "        plt.plot(self.kss,self.css,'*r', label = 'Steady state')\n",
+    "        \n",
+    "        # Add arrows ilustrating behavior in different parts of\n",
+    "        # the diagram.\n",
+    "        # Taken from:\n",
+    "        # http://systems-sciences.uni-graz.at/etextbook/sw2/phpl_python.html\n",
+    "        if arrows:\n",
+    "            x = np.linspace(k[0],k[-1],n_arrows)\n",
+    "            y = np.linspace(self.cFunc(k[0]),self.cFunc(k[-1]),n_arrows)\n",
+    "            \n",
+    "            X, Y = np.meshgrid(x,y)\n",
+    "            dc = self.dcdt(Y,X)\n",
+    "            dk = self.dkdt(Y,X)\n",
+    "            \n",
+    "            M = (np.hypot(dk, dc))\n",
+    "            M[ M == 0] = 1.\n",
+    "            dk /= M\n",
+    "            dc /= M\n",
+    "            plt.quiver(X, Y, dk, dc, M, pivot='mid', alpha = 0.3)\n",
+    "        \n",
+    "        # Labels\n",
+    "        if labels:\n",
+    "            plt.title('Phase diagram and consumption rule\\n(normalized by efficiency units)')\n",
+    "            plt.xlabel('k')\n",
+    "            plt.ylabel('c')\n",
+    "        if legend:\n",
+    "            plt.legend()\n",
+    "        \n",
+    "    \n",
+    "    def J_matrix(self,c,k):\n",
+    "        \"\"\"\n",
+    "        Returns the matrix of first derivatives of the solution's dynamic system\n",
+    "        evaluated at the point (c,k).\n",
+    "        This matrix is used for linear approximations of the system around point\n",
+    "        (c,k).\n",
+    "        \"\"\"\n",
+    "        \n",
+    "        J = np.array([[1/self.rho*(self.alpha*k**(self.alpha - 1)-\\\n",
+    "                                   self.theta-self.xi-self.delta-self.phi),\\\n",
+    "                       c/self.rho*\\\n",
+    "                       self.alpha*(self.alpha - 1)*k**(self.alpha - 2)],\n",
+    "                      [-1,\n",
+    "                       self.alpha*k**(self.alpha-1) -\\\n",
+    "                       (self.phi + self.xi +self.delta)]])\n",
+    "        \n",
+    "        return(J)\n",
+    "    \n",
+    "    def slope_ss(self):\n",
+    "        \"\"\"\n",
+    "        Finds the slope of the saddle path at the steady state.\n",
+    "        \"\"\"\n",
+    "        J = self.J_matrix(self.css,self.kss)\n",
+    "        \n",
+    "        # Find eigenvalues and eigenvectors\n",
+    "        w, v = LA.eig(J)\n",
+    "        # Find position of smallest eigenvalue\n",
+    "        min_eig = np.argsort(w)[0]\n",
+    "        \n",
+    "        # The slope of the saddle path is that\n",
+    "        # generated by the eigenvector of the\n",
+    "        # negative eigenvalue.\n",
+    "        slope = v[0,min_eig]/v[1,min_eig]\n",
+    "        \n",
+    "        return(slope)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## _Example_\n",
+    "\n",
+    "This is a quick example of how the class is used.\n",
+    "\n",
+    "An instance of the model is first created by assigning the required parameter values.\n",
+    "\n",
+    "The model needs to be solved in order to find the consumption rule or 'saddle path'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Consumption at k = 1.13 is c = 0.75\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create and solve model\n",
+    "RCKmodExample = RCKmod(rho = 2,alpha = 0.3,theta = 0.02,xi = 0.01,\n",
+    "                       delta = 0.08,phi = 0.03)\n",
+    "RCKmodExample.solve()\n",
+    "\n",
+    "# Test the consumption rule\n",
+    "print('Consumption at k = %1.2f is c = %1.2f'\\\n",
+    "      % (RCKmodExample.kss/2, RCKmodExample.cFunc(RCKmodExample.kss/2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model's phase diagram can then be generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RCKmodExample.phase_diagram(arrows= True, n_arrows = 12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The $\\texttt{RCKmod}$ class can also be used to simulate the dynamics of capital given a starting point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create grid of time points\n",
+    "t = np.linspace(0,100,100)\n",
+    "\n",
+    "# Find capital dynamics at the desired time points and with\n",
+    "# a given starting capital\n",
+    "k0 = 4\n",
+    "k = RCKmodExample.k_dynamics(k0,t)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure()\n",
+    "plt.plot(t,k)\n",
+    "plt.axhline(y = RCKmodExample.kss,linestyle = '--',color = 'k',\n",
+    "            label = '$\\\\bar{k}$')\n",
+    "plt.title('Capital')\n",
+    "plt.xlabel('Time')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With capital, the consumption rule can be used to find the dynamics of consumption."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Find consumption\n",
+    "c = RCKmodExample.cFunc(k)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure()\n",
+    "plt.plot(t,c)\n",
+    "plt.axhline(y = RCKmodExample.css,linestyle = '--',color = 'k',\n",
+    "            label = '$\\\\bar{c}$')\n",
+    "plt.title('Consumption')\n",
+    "plt.xlabel('Time')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appendix 1: finding the slope of the saddle path at the steady state\n",
+    "\n",
+    "From the solution of the model, we know that the system of differential equations that describes the dynamics of $c$ and $k$ is \n",
+    "\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\dot{c_t}\\\\\n",
+    "\\dot{k_t}\n",
+    "\\end{bmatrix}\n",
+    "=\n",
+    "\\begin{bmatrix}\n",
+    "f(c_t,k_t)\\\\\n",
+    "g(c_t,k_t)\n",
+    "\\end{bmatrix}\n",
+    "=\n",
+    "\\begin{bmatrix}\n",
+    "\\frac{c_t}{\\rho}(\\alpha k_t^{\\alpha - 1} - \\theta - \\xi - \\delta) - \\phi\\\\\n",
+    "k_t^\\alpha - c_t - (\\phi + \\xi + \\delta)*k_t\n",
+    "\\end{bmatrix}\n",
+    "\\end{align}\n",
+    "\n",
+    "We seek to approximate this system around the steady state $(\\bar{c},\\bar{k})$ through\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\dot{c_t}\\\\\n",
+    "\\dot{k_t}\n",
+    "\\end{bmatrix}\n",
+    "\\approx\n",
+    "\\begin{bmatrix}\n",
+    "f(\\bar{c},\\bar{k}) + f_c(\\bar{c},\\bar{k})(c_t - \\bar{c}) + f_k(\\bar{c},\\bar{k})(k_t - \\bar{k})\\\\\n",
+    "g(\\bar{c},\\bar{k}) + g_c(\\bar{c},\\bar{k})(c_t - \\bar{c}) + g_k(\\bar{c},\\bar{k})(k_t - \\bar{k})\n",
+    "\\end{bmatrix}\n",
+    "\\end{align}\n",
+    "\n",
+    "For this we find the system's matrix of first derivatives\n",
+    "\n",
+    "\\begin{align}\n",
+    "J(c,k) =\n",
+    "\\begin{bmatrix}\n",
+    "f_c(c,k) & f_k(c,k)\\\\\n",
+    "g_c(c,k) & g_k(c,k)\n",
+    "\\end{bmatrix} = \n",
+    "\\begin{bmatrix}\n",
+    "\\frac{1}{\\rho}(\\alpha k^{\\alpha - 1} - \\theta - \\xi - \\delta) - \\phi & \\frac{c}{\\rho}\\alpha (\\alpha-1) k^{\\alpha - 2}\\\\\n",
+    "-1 & \\alpha k^{\\alpha - 1} - (\\phi + \\xi + \\delta)\n",
+    "\\end{bmatrix}\n",
+    "\\end{align}\n",
+    "\n",
+    "Given the saddle-path stability of the system, $J(c_{ss},k_{ss})$ will have a positive and a negative eigenvalue. The slope of the saddle path at the steady state capital is given by the eigenvector associated with the negative eigenvalue.\n",
+    "\n",
+    "To understand why, rewrite the ODE system as\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\dot{c_t}\\\\\n",
+    "\\dot{k_t}\n",
+    "\\end{bmatrix}\n",
+    "\\approx\n",
+    "\\begin{bmatrix}\n",
+    "f(\\bar{c},\\bar{k})\\\\\n",
+    "g(\\bar{c},\\bar{k})\n",
+    "\\end{bmatrix}+ J(\\bar{c},\\bar{k})\\times\n",
+    "\\begin{bmatrix}\n",
+    "c_t - \\bar{c}\\\\\n",
+    "k_t - \\bar{k}\n",
+    "\\end{bmatrix}.\n",
+    "\\end{align}\n",
+    "\n",
+    "Since $(\\bar{c},\\bar{k})$ are the steady state values, we know $f(\\bar{c},\\bar{k}) = g(\\bar{c},\\bar{k}) = 0$. Now define $\\hat{c} = c - \\bar{c}$ and $\\hat{k} = k - \\bar{k}$, and notice $\\dot{c} = \\dot{\\hat{c}}$ and $\\dot{k} = \\dot{\\hat{k}}$. With these facts and writting $J(\\bar{c},\\bar{k}) = J$ to reduce clutter, we have\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\dot{\\hat{c}_t}\\\\\n",
+    "\\dot{\\hat{k}_t}\n",
+    "\\end{bmatrix}\n",
+    "\\approx\n",
+    "J\\times\n",
+    "\\begin{bmatrix}\n",
+    "\\hat{c}_t\\\\\n",
+    "\\hat{k}_t\n",
+    "\\end{bmatrix},\n",
+    "\\end{align}\n",
+    "\n",
+    "which is a first-order homogeneous ODE system (see ['Matrix differential equation' in Wikipedia](https://en.wikipedia.org/wiki/Matrix_differential_equation)). As discussed in the Wikipedia entry, these systems have solutions of the form\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\hat{c}_t\\\\\n",
+    "\\hat{k}_t\n",
+    "\\end{bmatrix}\n",
+    "=\n",
+    "c_1 e^{\\lambda_1 t}\\mathbf{u_1} + c_2 e^{\\lambda_2 t}\\mathbf{u_2}\n",
+    "\\end{align}\n",
+    "\n",
+    "where $c_1$ and $c_2$ are constants, $\\lambda_1$ and $\\lambda_2$ are the eigenvalues of $J$, and $\\mathbf{u_1}$ and $\\mathbf{u_2}$ are their associated eigenvectors.\n",
+    "\n",
+    "For this model, it can be shown that (for all reasonable parameter values) $J$ has a positive and a negative eigenvalue. Thus, assume $\\lambda_1 < 0$ and $\\lambda_2 > 0$.\n",
+    "\n",
+    "We are interested in solutions that approximate to the steady state ($\\hat{c}_t = \\hat{k}_t =0$) as $t \\rightarrow \\infty$. For these solutions, we must set $u_2 = 0$: else, given $\\lambda_2 > 0$, $e^{\\lambda_2 t}\\rightarrow \\infty$ as $t \\rightarrow \\infty$ and the system diverges. Therefore, we are left with a solution of the type\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\hat{c}_t\\\\\n",
+    "\\hat{k}_t\n",
+    "\\end{bmatrix}\n",
+    "=\n",
+    "c_1 e^{\\lambda_1 t}\\mathbf{u_1} =\n",
+    "\\begin{bmatrix}\n",
+    "c_1 e^{\\lambda_1 t} u_{1,1}\\\\\n",
+    "c_1 e^{\\lambda_1 t} u_{1,2}\n",
+    "\\end{bmatrix}.\n",
+    "\\end{align}\n",
+    "\n",
+    "Note that, given $\\lambda_1<0$, as $t \\rightarrow \\infty$, $e^{\\lambda_1 t}\\rightarrow 0$ and $[\\hat{c}_t,\\hat{k}_t] = [0,0]$ which is precisely what we require.\n",
+    "\n",
+    "From the previous solution, we know that in our linear approximation of the dynamic system around $[\\hat{c}_t, \\hat{k}_t] = [0,0]$, the ratio $\\hat{c}_t/\\hat{k}_t$ will be the constant $u_{1,1}/u_{1,2}$. Therefore, we can conclude that the slope of the tangent to the saddle path (in k-c coordinates) at the steady state capital $\\bar{k}$ will be exactly $u_{1,1}/u_{1,2}$ where $\\mathbf{u_1}$ is the eigenvector associated with the negative eigenvalue of the Jacobian matrix J.  This corresponds to the \"time elimination\" step, because $u_{1,1}$ corresponds to $dc/dt$ and $u_{2,1}$ corresponds to $dk/dt$, so $u_{1,1}/u_{1,2}$ corresponds to $(dc/dt)/(dk/dt)=dc/dk$, which is the slope of the consumption function at the steady state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appendix 2: Figures for Christopher D. Carroll's lecture notes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "code_folding": [
+     8
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:3: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:3: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:3: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:3: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "/var/folders/2d/q7h3srw57c7blcw4gtj61xk80000gn/T/ipykernel_5528/787271180.py:3: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  labels = ['$\\phi$ low','$\\phi$ high']\n",
+      "/var/folders/2d/q7h3srw57c7blcw4gtj61xk80000gn/T/ipykernel_5528/787271180.py:3: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  labels = ['$\\phi$ low','$\\phi$ high']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure RamseySSPlot\n",
+    "\n",
+    "labels = ['$\\phi$ low','$\\phi$ high']\n",
+    "colors = ['red','blue']\n",
+    "g = [0.01,0.1]\n",
+    "npoints = 100\n",
+    "\n",
+    "plt.figure()\n",
+    "for i in range(len(g)):\n",
+    "    \n",
+    "    # Create model\n",
+    "    RCKmodExample = RCKmod(rho = 2,alpha = 0.3,theta = 0.02,xi = 0.01,\n",
+    "                           delta = 0.08,phi = g[i])\n",
+    "    \n",
+    "    # K range\n",
+    "    k = np.linspace(0,RCKmodExample.kmax,npoints)\n",
+    "        \n",
+    "    # Plot k0 locus\n",
+    "    plt.plot(k,RCKmodExample.k0locus(k),color = colors[i])\n",
+    "    \n",
+    "    k_dummy = (RCKmodExample.kmax + RCKmodExample.kss) / 2\n",
+    "    \n",
+    "    plt.annotate('$\\\\dot{k}=0$, ' + labels[i],\n",
+    "                 xy=(k_dummy, RCKmodExample.k0locus(k_dummy)),\n",
+    "                 xytext=(k_dummy, RCKmodExample.k0locus(k_dummy)*1.3),\n",
+    "                 arrowprops = dict(color = 'black', width = 0.05,\n",
+    "                                   headwidth = 5, headlength = 7))\n",
+    "    # Plot c0 locus\n",
+    "    plt.axvline(x = RCKmodExample.kss,linestyle = '--',color = colors[i])\n",
+    "    \n",
+    "    plt.annotate('$\\\\dot{c} = 0$, ' + labels[i],\n",
+    "                 xy=(RCKmodExample.kss, RCKmodExample.css/3),\n",
+    "                 xytext=(RCKmodExample.kss + 2, RCKmodExample.css/3),\n",
+    "                 arrowprops = dict(color = 'black', width = 0.05,\n",
+    "                                   headwidth = 5, headlength = 7))\n",
+    "\n",
+    "# Labels\n",
+    "plt.title('$\\\\dot{c}/c = 0$ and $\\\\dot{k} = 0$ Loci')\n",
+    "plt.xlabel('k')\n",
+    "plt.ylabel('c')\n",
+    "fig = plt.gcf() # Get the figure in order to save it\n",
+    "# fig.savefig('./RamseyCassKoopmans-Figures/RamseySSPlot.svg')\n",
+    "# fig.savefig('./RamseyCassKoopmans-Figures/RamseySSPlot.png')\n",
+    "# fig.savefig('./RamseyCassKoopmans-Figures/RamseySSPlot.pdf')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure RamseySaddlePlot\n",
+    "npoints = 100\n",
+    "\n",
+    "# Create and solve model\n",
+    "RCKmodExample = RCKmod(rho = 2,alpha = 0.3,theta = 0.02,xi = 0.01,\n",
+    "                       delta = 0.08,phi = 0.03)\n",
+    "RCKmodExample.solve()\n",
+    "pd = RCKmodExample.phase_diagram(arrows= True, n_arrows = 12, labels = False)\n",
+    "\n",
+    "# Set initial k for off-path trajectories\n",
+    "k0 = RCKmodExample.kss * 0.35\n",
+    "\n",
+    "# Find values of c: one too high, one too low\n",
+    "c_high = RCKmodExample.cFunc(k0) * 1.25\n",
+    "c_low = RCKmodExample.cFunc(k0) * 0.75\n",
+    "init_cs = [c_low, c_high]\n",
+    "\n",
+    "# Trajectories with high consumption values become problematic much faster\n",
+    "# than those with low consumption, thus, different time intervals are used\n",
+    "t_final = [20,3.4602]\n",
+    "\n",
+    "for i in range(len(init_cs)):\n",
+    "        \n",
+    "    # Create dynamic system for c and k\n",
+    "    dck_dt = lambda ck, t : [RCKmodExample.dcdt(ck[0],ck[1]),\n",
+    "                             RCKmodExample.dkdt(ck[0],ck[1])]\n",
+    "    \n",
+    "    # Create time vector\n",
+    "    t = np.linspace(0,t_final[i],npoints)\n",
+    "    \n",
+    "    # Solve for c and k\n",
+    "    ck = odeint(dck_dt, [init_cs[i], k0], t)\n",
+    "    \n",
+    "    # Plot the trajectory in k-c space\n",
+    "    plt.plot(ck[:,1], ck[:,0], 'k')\n",
+    "    \n",
+    "plt.title('Transition to the Steady State')\n",
+    "plt.xlabel('k')\n",
+    "plt.ylabel('c')\n",
+    "fig = plt.gcf() # Get the figure in order to save it\n",
+    "# fig.savefig('./RamseyCassKoopmans-Figures/RamseySaddlePlot.svg')\n",
+    "# fig.savefig('./RamseyCassKoopmans-Figures/RamseySaddlePlot.png')\n",
+    "# fig.savefig('./RamseyCassKoopmans-Figures/RamseySaddlePlot.pdf')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "encoding": "# -*- coding: utf-8 -*-",
+   "formats": "ipynb,py:percent"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/The-Prescott-Real-Business-Cycle-RBC.ipynb b/notebooks/The-Prescott-Real-Business-Cycle-RBC.ipynb
new file mode 100644
index 00000000..9ce5d100
--- /dev/null
+++ b/notebooks/The-Prescott-Real-Business-Cycle-RBC.ipynb
@@ -0,0 +1,2788 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Prescott Real Business Cycle DSGE Model \n",
+    "\n",
+    "[![badge](https://img.shields.io/badge/Launch%20using%20-Econ--ARK-blue)](https://econ-ark.org/materials/the-prescott-real-business-cycle-rbc#launch)\n",
+    "\n",
+    "\n",
+    "\n",
+    "This handout presents the elements of the original Real Business Cycle model of\n",
+    "aggregate fluctuations, as laid out by Prescott (1986) and as outlined in Christopher Carroll's lecture notes.__[RBC-Prescott](http://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/DSGEModels/RBC-Prescott.pdf)__.\n",
+    "\n",
+    "This notebook demonstrates how to setup, solve, and estimate a simple real business cycle model in Python. The model is very standard; the setup and notation here corresponds to one seen in Chris Carroll's lecture notes.\n",
+    "\n",
+    "The observational results of the Ramey-Francis (2009) are presented in this notebook __[Notebook-Ramey-Francis(2009)](https://github.com/ani1231091/Real-Business-Cycle-)__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "The raw code for this RBC notebook is by default hidden for easier reading.\n",
+       "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Initials\n",
+    "%matplotlib inline\n",
+    "from __future__ import division\n",
+    "\n",
+    "import numpy as np\n",
+    "from scipy import optimize, signal\n",
+    "import pandas as pd\n",
+    "from pandas_datareader.data import DataReader\n",
+    "import statsmodels.api as sm\n",
+    "from statsmodels.tools.numdiff import approx_fprime, approx_fprime_cs\n",
+    "from IPython.display import HTML, display\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# import seaborn as sn\n",
+    "from numpy.testing import assert_allclose\n",
+    "\n",
+    "# Set some pretty-printing options\n",
+    "np.set_printoptions(precision=3, suppress=True, linewidth=120)\n",
+    "pd.set_option('float_format', lambda x: '%.3g' % x, )\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "The raw code for this RBC notebook is by default hidden for easier reading.\n",
+    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.''')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some Theory\n",
+    "\n",
+    "King, Plosser, and Rebelo (1988) characterize the set of utility functions such that the model admits a Balanced Growth Path.\n",
+    "With labor-augmenting technical progress, and hence ever increasing wages, there are income and substitution effects.\n",
+    "The income and substitution effects cancel each other if and only if\n",
+    "$$u(c_t, z_t) = \\log(c_t) + v(z_t) $$ or\n",
+    "$$u(c_t, z_t)=\\frac{(c)^(1-\\sigma)}{1-\\sigma}v(z_t)$$\n",
+    "where v(z)=$\\psi$ (1-l), $\\psi$ >0, $0\\leq l \\leq 1$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Household problem\n",
+    "\n",
+    "$$\n",
+    "\\max E_0 \\sum_{t=0}^\\infty \\beta^t u(c_t, z_t)\n",
+    "$$\n",
+    "\n",
+    "s.t:\n",
+    "\n",
+    "- the budget constraint: $y_t = c_t + i_t$\n",
+    "- the capital accumulation equation: $k_{t+1} = (1 - \\delta) k_t + i_t$\n",
+    "- $1 = l_t + z_t$\n",
+    "\n",
+    "where households have the following production technology:\n",
+    "\n",
+    "$$y_t = a_t f(k_t, l_t)$$\n",
+    "\n",
+    "and where the (log of the) technology process follows an AR(1) process:\n",
+    "\n",
+    "$$\n",
+    "\\log a_t = \\rho \\log a_{t-1} + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)\n",
+    "$$\n",
+    "\n",
+    "## Functional forms\n",
+    "\n",
+    "We will assume additively separable utility in consumption and leisure (Prescott 1986,King-Rebello-Plosser 1988), with:\n",
+    "\n",
+    "$$u(c_t, z_t) = \\log(c_t) + v(z_t) $$\n",
+    "\n",
+    "where v(z)=$\\psi$ (1-l), $\\psi$ >0, $0\\leq l \\leq 1$.\n",
+    "\n",
+    "The production function will be Cobb-Douglas:\n",
+    "\n",
+    "$$f(k_t, l_t) = k_t^\\alpha l_t^{1 - \\alpha}$$\n",
+    "where $\\alpha$ is the marginal product of capital. Later for simulation and calibration purposes, we ll place a value on $\\alpha$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## FOC Solving\n",
+    "\n",
+    "Two equations characterize the FOC for the infinite time horizon. The first one for choosing between leisure and consumption, the second one is the classic euler equation for consumption.\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\psi & = \\frac{1}{c_t} (1 - \\alpha) a_t \\left ( \\frac{k_t}{l_t} \\right )^{\\alpha} & \\text{Consumption-Leisure Choice} \\\\\n",
+    "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha a_{t+1} \\left ( \\frac{k_{t+1}}{l_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} & \\text{Euler Equation for Consumption} \\\\\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Equations characterizing the system\n",
+    "\n",
+    "Collecting all of the model equations, we have:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\psi c_t & = (1 - \\alpha) a_t \\left ( \\frac{k_t}{l_t} \\right )^{\\alpha} & \\text{Static FOC} \\\\\n",
+    "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha a_{t+1} \\left ( \\frac{k_{t+1}}{l_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} & \\text{Euler equation} \\\\\n",
+    "y_t & = a_t k_t^\\alpha l_t^{1 - \\alpha} & \\text{Production function} \\\\\n",
+    "y_t & = c_t + i_t & \\text{Aggregate resource constraint} \\\\\n",
+    "k_{t+1} & = (1 - \\delta) k_t + i_t & \\text{Captial accumulation} \\\\\n",
+    "y_t & = c_t + k_{t+1}-(1 - \\delta) k_t & \\text{The combined resource constraint}\\\\\n",
+    "1 & = l_t + z_t & \\text{Labor-leisure tradeoff} \\\\\n",
+    "\\log a_t & = \\rho \\log a_{t-1} + \\varepsilon_t & \\text{Technology shock transition}\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "### Variables / Parameters\n",
+    "\n",
+    "At this point, it becomes important to distinguish between the different types of variables that are involved in these equations. Jumpers and ...\n",
+    "This system is in the following variables:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "x_t = \\{ \\quad & &\\\\\n",
+    "& y_t, & \\text{Output}\\\\\n",
+    "& c_t, & \\text{Consumption}\\\\\n",
+    "& i_t, & \\text{Investment}\\\\\n",
+    "& l_t, & \\text{Labor}\\\\\n",
+    "& z_t, & \\text{Leisure}\\\\\n",
+    "& k_t, & \\text{Capital}\\\\\n",
+    "& a_t  & \\text{Technology} \\\\\n",
+    "\\} \\quad & &\n",
+    "\\end{align}$$\n",
+    "\n",
+    "and depends on the following parameters:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\{ \\quad & &\\\\\n",
+    "& \\beta, & \\text{Discount rate}\\\\\n",
+    "& \\psi, & \\text{Marginal disutility of labor}\\\\\n",
+    "& \\delta, & \\text{Depreciation rate}\\\\\n",
+    "& \\alpha, & \\text{Capital-share of output}\\\\\n",
+    "& \\rho, & \\text{Technology shock persistence}\\\\\n",
+    "& \\sigma^2 & \\text{Technology shock variance}\\\\\n",
+    "\\} \\quad & &\n",
+    "\\end{align}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the names of the equations, variables, and parameters\n",
+    "equation_names = [\n",
+    "    'static FOC', 'euler equation', 'production',\n",
+    "    'aggregate resource constraint', 'capital accumulation',\n",
+    "    'labor-leisure', 'technology shock transition'\n",
+    "]\n",
+    "variable_names = [\n",
+    "    'output', 'consumption', 'investment',\n",
+    "    'labor', 'leisure', 'capital', 'technology'\n",
+    "]\n",
+    "parameter_names = [\n",
+    "    'discount rate', 'marginal disutility of labor',\n",
+    "    'depreciation rate', 'capital share',\n",
+    "    'technology shock persistence',\n",
+    "    'technology shock standard deviation',\n",
+    "]\n",
+    "\n",
+    "# Save some symbolic forms for pretty-printing\n",
+    "variable_symbols = [\n",
+    "    r\"y\", r\"c\", r\"i\", r\"l\", r\"z\", r\"k\", r\"a\",\n",
+    "]\n",
+    "contemporaneous_variable_symbols = [\n",
+    "    r\"$%s_t$\" % symbol for symbol in variable_symbols\n",
+    "]\n",
+    "lead_variable_symbols = [\n",
+    "    r\"$%s_{t+1}$\" % symbol for symbol in variable_symbols\n",
+    "]\n",
+    "\n",
+    "parameter_symbols = [\n",
+    "    r\"$\\beta$\", r\"$\\psi$\", r\"$\\delta$\", r\"$\\alpha$\", r\"$\\rho$\", r\"$\\sigma^2$\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numerical method\n",
+    "\n",
+    "Using these equations, we can numerically find the steady-state using a root-finding algorithm and can then log-linearize around the steady-state using a numerical gradient procedure. In particular, here we follow DeJong and Dave (2011) chapter 2 and 3.1.\n",
+    "\n",
+    "Write the generic non-linear system as:\n",
+    "\n",
+    "$$\n",
+    "\\Gamma(E_t x_{t+1}, x_t, z_t) = 0\n",
+    "$$\n",
+    "\n",
+    "in the absense of shocks, this can be rewritten as:\n",
+    "\n",
+    "$$\n",
+    "\\Psi(x_{t+1}, x_t) = 0\n",
+    "$$\n",
+    "\n",
+    "or as:\n",
+    "\n",
+    "$$\n",
+    "\\Psi_1(x_{t+1}, x_t) = \\Psi_2(x_{t+1}, x_t)\n",
+    "$$\n",
+    "\n",
+    "and finally in logs as\n",
+    "\n",
+    "$$\n",
+    "\\log \\Psi_1(e^{\\log x_{t+1}}, e^{\\log x_t}) - \\log \\Psi_2(e^{\\log x_{t+1}}, e^{\\log x_t}) = 0\n",
+    "$$\n",
+    "\n",
+    "First, we define a new class (`RBC1`) which holds the state of the RBC model (its dimensions, parameters, etc) and has methods for evaluating the log system. The `eval_logged` method evaluates the last equation, above.\n",
+    "\n",
+    "Notice that the order of variables and order of equations is as described above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "class RBC1(object):\n",
+    "    def __init__(self, params=None):\n",
+    "        # Model dimensions\n",
+    "        self.k_params = 6\n",
+    "        self.k_variables = 7\n",
+    "        \n",
+    "        # Initialize parameters\n",
+    "        if params is not None:\n",
+    "            self.update(params)\n",
+    "    \n",
+    "    def update(self, params):\n",
+    "        # Save deep parameters\n",
+    "        self.discount_rate = params[0]\n",
+    "        self.disutility_labor = params[1]\n",
+    "        self.depreciation_rate = params[2]\n",
+    "        self.capital_share = params[3]\n",
+    "        self.technology_shock_persistence = params[4]\n",
+    "        self.technology_shock_std = params[5]\n",
+    "        \n",
+    "    def eval_logged(self, log_lead, log_contemporaneous):\n",
+    "        (log_lead_output, log_lead_consumption, log_lead_investment,\n",
+    "         log_lead_labor, log_lead_leisure, log_lead_capital,\n",
+    "         log_lead_technology_shock) = log_lead\n",
+    "        \n",
+    "        (log_output, log_consumption, log_investment, log_labor,\n",
+    "         log_leisure, log_capital, log_technology_shock) = log_contemporaneous\n",
+    "        \n",
+    "        return np.r_[\n",
+    "            self.log_static_foc(\n",
+    "                log_lead_consumption, log_lead_labor,\n",
+    "                log_lead_capital, log_lead_technology_shock\n",
+    "            ),\n",
+    "            self.log_euler_equation(\n",
+    "                log_lead_consumption, log_lead_labor,\n",
+    "                log_lead_capital, log_lead_technology_shock,\n",
+    "                log_consumption\n",
+    "            ),\n",
+    "            self.log_production(\n",
+    "                log_lead_output, log_lead_labor, log_lead_capital,\n",
+    "                log_lead_technology_shock\n",
+    "            ),\n",
+    "            self.log_aggregate_resource_constraint(\n",
+    "                log_lead_output, log_lead_consumption,\n",
+    "                log_lead_investment\n",
+    "            ),\n",
+    "            self.log_capital_accumulation(\n",
+    "                log_lead_capital, log_investment, log_capital\n",
+    "            ),\n",
+    "            self.log_labor_leisure_constraint(\n",
+    "                log_lead_labor, log_lead_leisure\n",
+    "            ),\n",
+    "            self.log_technology_shock_transition(\n",
+    "                log_lead_technology_shock, log_technology_shock\n",
+    "            )\n",
+    "        ]\n",
+    "    \n",
+    "    def log_static_foc(self, log_lead_consumption, log_lead_labor,\n",
+    "                       log_lead_capital, log_lead_technology_shock):\n",
+    "        return (\n",
+    "            np.log(self.disutility_labor) +\n",
+    "            log_lead_consumption -\n",
+    "            np.log(1 - self.capital_share) -\n",
+    "            log_lead_technology_shock -\n",
+    "            self.capital_share * (log_lead_capital - log_lead_labor)\n",
+    "        )\n",
+    "        \n",
+    "    def log_euler_equation(self, log_lead_consumption, log_lead_labor,\n",
+    "                           log_lead_capital, log_lead_technology_shock,\n",
+    "                           log_consumption):\n",
+    "        return (\n",
+    "            -log_consumption -\n",
+    "            np.log(self.discount_rate) +\n",
+    "            log_lead_consumption -\n",
+    "            np.log(\n",
+    "                (self.capital_share *\n",
+    "                 np.exp(log_lead_technology_shock) * \n",
+    "                 np.exp((1 - self.capital_share) * log_lead_labor) /\n",
+    "                 np.exp((1 - self.capital_share) * log_lead_capital)) +\n",
+    "                (1 - self.depreciation_rate)\n",
+    "            )\n",
+    "        )\n",
+    "        \n",
+    "    def log_production(self, log_lead_output, log_lead_labor, log_lead_capital,\n",
+    "                       log_lead_technology_shock):\n",
+    "        return (\n",
+    "            log_lead_output -\n",
+    "            log_lead_technology_shock -\n",
+    "            self.capital_share * log_lead_capital -\n",
+    "            (1 - self.capital_share) * log_lead_labor\n",
+    "        )\n",
+    "        \n",
+    "    def log_aggregate_resource_constraint(self, log_lead_output, log_lead_consumption,\n",
+    "                                          log_lead_investment):\n",
+    "        return (\n",
+    "            log_lead_output -\n",
+    "            np.log(np.exp(log_lead_consumption) + np.exp(log_lead_investment))\n",
+    "        )\n",
+    "    \n",
+    "    def log_capital_accumulation(self, log_lead_capital, log_investment, log_capital):\n",
+    "        return (\n",
+    "            log_lead_capital -\n",
+    "            np.log(np.exp(log_investment) + (1 - self.depreciation_rate) * np.exp(log_capital))\n",
+    "        )\n",
+    "    \n",
+    "    def log_labor_leisure_constraint(self, log_lead_labor, log_lead_leisure):\n",
+    "        return (\n",
+    "            -np.log(np.exp(log_lead_labor) + np.exp(log_lead_leisure))\n",
+    "        )\n",
+    "    \n",
+    "    def log_technology_shock_transition(self, log_lead_technology_shock, log_technology_shock):\n",
+    "        return (\n",
+    "            log_lead_technology_shock -\n",
+    "            self.technology_shock_persistence * log_technology_shock\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Later we will estimate (some of) the parameters; in the meantime we fix them at the values used to generate the datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <td>discount rate</td>\n",
+       "      <td>marginal disutility of labor</td>\n",
+       "      <td>depreciation rate</td>\n",
+       "      <td>capital share</td>\n",
+       "      <td>technology shock persistence</td>\n",
+       "      <td>technology shock standard deviation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <td>0.95</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.76</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   0                             1                  2  \\\n",
+       "name   discount rate  marginal disutility of labor  depreciation rate   \n",
+       "value           0.95                             3              0.025   \n",
+       "\n",
+       "                   3                             4  \\\n",
+       "name   capital share  technology shock persistence   \n",
+       "value           0.36                          0.85   \n",
+       "\n",
+       "                                         5  \n",
+       "name   technology shock standard deviation  \n",
+       "value                                 0.76  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Setup fixed parameters\n",
+    "parameters = pd.DataFrame({\n",
+    "    'name': parameter_names,\n",
+    "    'value': [0.95, 3, 0.025, 0.36, 0.85, 0.76]\n",
+    "})\n",
+    "parameters.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Steady state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numeric calculation\n",
+    "\n",
+    "To numerically calculate steady-state, we apply a root-finding algorithm to the `eval_logged` method. In particular, we are finding values $\\bar x$ such that\n",
+    "\n",
+    "$$\\log \\Psi_1(e^{\\log \\bar x}, e^{\\log \\bar x}) - \\log \\Psi_2(e^{\\log \\bar x}, e^{\\log \\bar x}) = 0$$\n",
+    "\n",
+    "These will be confirmed analytically, below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we create a derived class, `RBC2` which extends all of the functionality from above, but now includes methods for numerical calcualtion of the steady-state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>output</th>\n",
+       "      <th>consumption</th>\n",
+       "      <th>investment</th>\n",
+       "      <th>labor</th>\n",
+       "      <th>leisure</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>technology</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.506</td>\n",
+       "      <td>0.0663</td>\n",
+       "      <td>0.241</td>\n",
+       "      <td>0.759</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       output  consumption  investment  labor  leisure  capital  technology\n",
+       "value   0.572        0.506      0.0663  0.241    0.759     2.65           1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class RBC2(RBC1):\n",
+    "    def steady_state_numeric(self):\n",
+    "        # Setup starting parameters\n",
+    "        log_start_vars = [0.5] * self.k_variables  # very arbitrary\n",
+    "\n",
+    "        # Setup the function the evaluate\n",
+    "        eval_logged = lambda log_vars: self.eval_logged(log_vars, log_vars)\n",
+    "\n",
+    "        # Apply the root-finding algorithm\n",
+    "        result = optimize.root(eval_logged, log_start_vars)\n",
+    "        \n",
+    "        return np.exp(result.x)\n",
+    "\n",
+    "mod2 = RBC2(parameters['value'])\n",
+    "\n",
+    "steady_state = pd.DataFrame({\n",
+    "    'value': mod2.steady_state_numeric()\n",
+    "}, index=variable_names)\n",
+    "\n",
+    "steady_state.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analytic evaluation\n",
+    "\n",
+    "In this case, we can analytically evaluate the steady-state:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\frac{\\bar y}{\\bar n} & = \\eta \\\\\n",
+    "\\frac{\\bar c}{\\bar n} & = \\eta - \\delta \\theta \\\\\n",
+    "\\frac{\\bar i}{\\bar n} & = \\delta \\theta \\\\\n",
+    "\\bar n & = \\frac{1}{\\left ( \\frac{1}{1 - \\alpha} \\right ) \\psi \\left [ 1 - \\delta \\theta^{1-\\alpha} \\right ]} \\\\\n",
+    "\\bar l & = 1 - \\bar n \\\\\n",
+    "\\frac{\\bar k}{\\bar n} & = \\theta\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "where\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\theta & = \\left ( \\frac{\\alpha}{1/\\beta - (1 - \\delta)} \\right )^\\frac{1}{1-\\alpha} \\\\\n",
+    "\\eta & = \\theta^\\alpha\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>output</th>\n",
+       "      <th>consumption</th>\n",
+       "      <th>investment</th>\n",
+       "      <th>labor</th>\n",
+       "      <th>leisure</th>\n",
+       "      <th>capital</th>\n",
+       "      <th>technology</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>numeric</th>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.506</td>\n",
+       "      <td>0.0663</td>\n",
+       "      <td>0.241</td>\n",
+       "      <td>0.759</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>analytic</th>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.506</td>\n",
+       "      <td>0.0663</td>\n",
+       "      <td>0.241</td>\n",
+       "      <td>0.759</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          output  consumption  investment  labor  leisure  capital  technology\n",
+       "numeric    0.572        0.506      0.0663  0.241    0.759     2.65           1\n",
+       "analytic   0.572        0.506      0.0663  0.241    0.759     2.65           1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class RBC3(RBC2):\n",
+    "    \n",
+    "    def update(self, params):\n",
+    "        # Update the deep parameters\n",
+    "        super(RBC3, self).update(params)\n",
+    "        \n",
+    "        # And now also calculate some intermediate parameters\n",
+    "        self.theta = (self.capital_share / (\n",
+    "            1 / self.discount_rate -\n",
+    "            (1 - self.depreciation_rate)\n",
+    "        ))**(1 / (1 - self.capital_share))\n",
+    "        \n",
+    "        self.eta = self.theta**self.capital_share\n",
+    "    \n",
+    "    def steady_state_analytic(self):\n",
+    "        steady_state = np.zeros(7)\n",
+    "\n",
+    "        # Labor (must be computed first)\n",
+    "        numer = (1 - self.capital_share) / self.disutility_labor\n",
+    "        denom = (1 - self.depreciation_rate * self.theta**(1 - self.capital_share))\n",
+    "        steady_state[3] = numer / denom\n",
+    "        # Output\n",
+    "        steady_state[0] = self.eta * steady_state[3]\n",
+    "        # Consumption\n",
+    "        steady_state[1] = (1 - self.capital_share) * self.eta / self.disutility_labor\n",
+    "        # Investment\n",
+    "        steady_state[2] = self.depreciation_rate * self.theta * steady_state[3]\n",
+    "        # Labor (computed already)\n",
+    "        # Leisure\n",
+    "        steady_state[4] = 1 - steady_state[3]\n",
+    "        # Capital\n",
+    "        steady_state[5] = self.theta * steady_state[3]\n",
+    "        # Technology shock\n",
+    "        steady_state[6] = 1\n",
+    "        \n",
+    "        return steady_state\n",
+    "    \n",
+    "mod3 = RBC3(parameters['value'])\n",
+    "\n",
+    "steady_state = pd.DataFrame({\n",
+    "    'numeric': mod3.steady_state_numeric(),\n",
+    "    'analytic': mod3.steady_state_analytic()\n",
+    "}, index=variable_names)\n",
+    "\n",
+    "steady_state.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Log-linearization\n",
+    "\n",
+    "The system we wrote down, above, was non-linear. In order to estimate it, we want to get it in a linear form:\n",
+    "\n",
+    "$$\n",
+    "A E_t \\tilde x_{t+1} = B \\tilde x_{t} + C v_{t+1}\n",
+    "$$\n",
+    "\n",
+    "where $v_{t+1}$ contains structural shocks (here, $z_t$ is included in the $x_t$ vector, and the only structural shock is $\\varepsilon_t$, the innovation to $z_t$).\n",
+    "\n",
+    "This can be achieved via log-linearization around the steady state. In this case, DeJong and Dave (2011) show that:\n",
+    "\n",
+    "$$\n",
+    "A = \\left [ \\frac{\\partial \\log [ \\Psi_1 ]}{\\partial \\log(x_{t+1})} (\\bar x) - \\frac{\\partial \\log [ \\Psi_2 ]}{\\partial \\log(x_{t+1})} (\\bar x) \\right ] \\\\\n",
+    "B = - \\left [ \\frac{\\partial \\log [ \\Psi_1 ]}{\\partial \\log(x_t)} (\\bar x) - \\frac{\\partial \\log [ \\Psi_2 ]}{\\partial \\log(x_t)} (\\bar x) \\right ] \\\\\n",
+    "$$\n",
+    "\n",
+    "where $\\tilde x_t = \\log \\left ( \\frac{x_t}{\\bar x} \\right )$ expresses the variables in proportional deviation from steady-state form.\n",
+    "\n",
+    "The matrix $C$ can be constructed by observation. In this case, we have:\n",
+    "\n",
+    "$$\n",
+    "C = \\begin{bmatrix} 0 & 0 & 0 & 0 & 0 & 0 & 1 \\end{bmatrix} \\qquad \\text{and} \\qquad v_{t+1} \\equiv \\varepsilon_t\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numeric calculation\n",
+    "\n",
+    "Since the `eval_logged` method of our class evaluates $\\log \\Psi_1(e^{\\log x_{t+1}}, e^{\\log x_t}) - \\log \\Psi_2(e^{\\log x_{t+1}}, e^{\\log x_t})$, we can apply a numerical gradient procedure to it to get $A$, when we differentiate with respect to the lead variables, and $B$, when we differentiate with respect to the contemporaneous variables.\n",
+    "\n",
+    "To learn about the numerical gradient procedure, one can check out the link __[here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.approx_fprime.html)__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$y_{t+1}$</th>\n",
+       "      <th>$c_{t+1}$</th>\n",
+       "      <th>$i_{t+1}$</th>\n",
+       "      <th>$l_{t+1}$</th>\n",
+       "      <th>$z_{t+1}$</th>\n",
+       "      <th>$k_{t+1}$</th>\n",
+       "      <th>$a_{t+1}$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>static FOC</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.36</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.0472</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0472</td>\n",
+       "      <td>-0.0737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.36</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aggregate resource constraint</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.884</td>\n",
+       "      <td>-0.116</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labor-leisure</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0.241</td>\n",
+       "      <td>-0.759</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology shock transition</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $l_{t+1}$  \\\n",
+       "static FOC                             0          1          0       0.36   \n",
+       "euler equation                         0          1          0    -0.0472   \n",
+       "production                             1          0          0      -0.64   \n",
+       "aggregate resource constraint          1     -0.884     -0.116          0   \n",
+       "capital accumulation                   0          0          0          0   \n",
+       "labor-leisure                         -0         -0         -0     -0.241   \n",
+       "technology shock transition            0          0          0          0   \n",
+       "\n",
+       "                               $z_{t+1}$  $k_{t+1}$  $a_{t+1}$  \n",
+       "static FOC                             0      -0.36         -1  \n",
+       "euler equation                         0     0.0472    -0.0737  \n",
+       "production                             0      -0.36         -1  \n",
+       "aggregate resource constraint          0          0          0  \n",
+       "capital accumulation                   0          1          0  \n",
+       "labor-leisure                     -0.759         -0         -0  \n",
+       "technology shock transition            0          0          1  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$y_t$</th>\n",
+       "      <th>$c_t$</th>\n",
+       "      <th>$i_t$</th>\n",
+       "      <th>$l_t$</th>\n",
+       "      <th>$z_t$</th>\n",
+       "      <th>$k_t$</th>\n",
+       "      <th>$a_t$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>static FOC</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aggregate resource constraint</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>0.975</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labor-leisure</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology shock transition</th>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>-0</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               $y_t$  $c_t$  $i_t$  $l_t$  $z_t$  $k_t$  $a_t$\n",
+       "static FOC                        -0     -0     -0     -0     -0     -0     -0\n",
+       "euler equation                    -0      1     -0     -0     -0     -0     -0\n",
+       "production                        -0     -0     -0     -0     -0     -0     -0\n",
+       "aggregate resource constraint     -0     -0     -0     -0     -0     -0     -0\n",
+       "capital accumulation              -0     -0  0.025     -0     -0  0.975     -0\n",
+       "labor-leisure                     -0     -0     -0     -0     -0     -0     -0\n",
+       "technology shock transition       -0     -0     -0     -0     -0     -0   0.85"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$\\varepsilon_t$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>static FOC</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aggregate resource constraint</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labor-leisure</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology shock transition</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               $\\varepsilon_t$\n",
+       "static FOC                                   0\n",
+       "euler equation                               0\n",
+       "production                                   0\n",
+       "aggregate resource constraint                0\n",
+       "capital accumulation                         0\n",
+       "labor-leisure                                0\n",
+       "technology shock transition                  1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class RBC4(RBC3):\n",
+    "    \n",
+    "    def A_numeric(self):\n",
+    "        log_steady_state = np.log(self.steady_state_analytic())\n",
+    "\n",
+    "        eval_logged_lead = lambda log_lead: self.eval_logged(log_lead, log_steady_state)\n",
+    "        \n",
+    "        return approx_fprime_cs(log_steady_state, eval_logged_lead)\n",
+    "\n",
+    "    def B_numeric(self):\n",
+    "        log_steady_state = np.log(self.steady_state_analytic())\n",
+    "        \n",
+    "        eval_logged_contemporaneous = lambda log_contemp: self.eval_logged(log_steady_state, log_contemp)\n",
+    "        \n",
+    "        return -approx_fprime_cs(log_steady_state, eval_logged_contemporaneous)\n",
+    "    \n",
+    "    def C(self):\n",
+    "        return np.r_[[0]*(self.k_variables-1), 1]\n",
+    "\n",
+    "mod4 = RBC4(parameters['value'])\n",
+    "        \n",
+    "display(pd.DataFrame(mod4.A_numeric(), index=equation_names, columns=lead_variable_symbols))\n",
+    "display(pd.DataFrame(mod4.B_numeric(), index=equation_names, columns=contemporaneous_variable_symbols))\n",
+    "display(pd.DataFrame(mod4.C(), index=equation_names, columns=[r'$\\varepsilon_t$']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analytic evaluation\n",
+    "\n",
+    "This system can be log-linearized directly, as well, yielding (see e.g. Ruge-Murcia (2007) Appendix A for these formulas, with slightly different notation):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\tilde c_t & = \\tilde z_t + \\alpha \\tilde k_t - \\alpha \\tilde n_t \\\\\n",
+    "-\\tilde c_t & = -E_t \\tilde c_{t+1} + \\zeta (\\alpha - 1) E_t \\tilde k_{t+1} + \\zeta (1 - \\alpha) E_t \\tilde n_{t+1} + \\zeta E_t \\tilde z_{t+1} \\\\\n",
+    "\\tilde y_t & = \\tilde z_t + \\alpha \\tilde k_t + (1 - \\alpha) \\tilde n_t \\\\\n",
+    "\\tilde y_t & = \\gamma \\tilde c_t + (1 - \\gamma) \\tilde i_t \\\\\n",
+    "\\tilde k_{t+1} & = (1 - \\delta) \\tilde k_t + \\delta \\tilde i_t \\\\\n",
+    "0 & = \\frac{\\bar l}{\\bar l + \\bar n} \\tilde l_{t+1} + \\frac{\\bar n}{\\bar l + \\bar n} \\tilde n_{t+1} \\\\\n",
+    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "where $\\zeta = \\alpha \\beta \\theta^{\\alpha - 1}$ and $\\gamma = 1 - \\delta \\theta^{1 - \\alpha}$ is the steady-state consumption-output ratio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$y_{t+1}$</th>\n",
+       "      <th>$c_{t+1}$</th>\n",
+       "      <th>$i_{t+1}$</th>\n",
+       "      <th>$l_{t+1}$</th>\n",
+       "      <th>$z_{t+1}$</th>\n",
+       "      <th>$k_{t+1}$</th>\n",
+       "      <th>$a_{t+1}$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>static FOC</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.36</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.0472</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0472</td>\n",
+       "      <td>-0.0737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.36</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aggregate resource constraint</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.884</td>\n",
+       "      <td>-0.116</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labor-leisure</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.241</td>\n",
+       "      <td>-0.759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology shock transition</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $l_{t+1}$  \\\n",
+       "static FOC                             0          1          0       0.36   \n",
+       "euler equation                         0          1          0    -0.0472   \n",
+       "production                             1          0          0      -0.64   \n",
+       "aggregate resource constraint          1     -0.884     -0.116          0   \n",
+       "capital accumulation                   0          0          0          0   \n",
+       "labor-leisure                          0          0          0     -0.241   \n",
+       "technology shock transition            0          0          0          0   \n",
+       "\n",
+       "                               $z_{t+1}$  $k_{t+1}$  $a_{t+1}$  \n",
+       "static FOC                             0      -0.36         -1  \n",
+       "euler equation                         0     0.0472    -0.0737  \n",
+       "production                             0      -0.36         -1  \n",
+       "aggregate resource constraint          0          0          0  \n",
+       "capital accumulation                   0          1          0  \n",
+       "labor-leisure                     -0.759          0          0  \n",
+       "technology shock transition            0          0          1  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$y_{t+1}$</th>\n",
+       "      <th>$c_{t+1}$</th>\n",
+       "      <th>$i_{t+1}$</th>\n",
+       "      <th>$l_{t+1}$</th>\n",
+       "      <th>$z_{t+1}$</th>\n",
+       "      <th>$k_{t+1}$</th>\n",
+       "      <th>$a_{t+1}$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>static FOC</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>production</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aggregate resource constraint</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.975</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>labor-leisure</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology shock transition</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $l_{t+1}$  \\\n",
+       "static FOC                             0          0          0          0   \n",
+       "euler equation                         0          1          0          0   \n",
+       "production                             0          0          0          0   \n",
+       "aggregate resource constraint          0          0          0          0   \n",
+       "capital accumulation                   0          0      0.025          0   \n",
+       "labor-leisure                          0          0          0          0   \n",
+       "technology shock transition            0          0          0          0   \n",
+       "\n",
+       "                               $z_{t+1}$  $k_{t+1}$  $a_{t+1}$  \n",
+       "static FOC                             0          0          0  \n",
+       "euler equation                         0          0          0  \n",
+       "production                             0          0          0  \n",
+       "aggregate resource constraint          0          0          0  \n",
+       "capital accumulation                   0      0.975          0  \n",
+       "labor-leisure                          0          0          0  \n",
+       "technology shock transition            0          0       0.85  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class RBC5(RBC4):\n",
+    "    \n",
+    "    def update(self, params):\n",
+    "        super(RBC5, self).update(params)\n",
+    "        \n",
+    "        # Now calculate some more intermediate parameters\n",
+    "        self.gamma = 1 - self.depreciation_rate * self.theta**(1 - self.capital_share)\n",
+    "        self.zeta = self.capital_share * self.discount_rate * self.theta**(self.capital_share - 1)\n",
+    "    \n",
+    "    def A_analytic(self):\n",
+    "        steady_state = self.steady_state_analytic()\n",
+    "        \n",
+    "        A = np.array([\n",
+    "            [0, 1, 0, self.capital_share, 0, -self.capital_share, -1],\n",
+    "            [0, 1, 0, self.zeta * (self.capital_share - 1), 0, self.zeta * (1 - self.capital_share), -self.zeta],\n",
+    "            [1, 0, 0, (self.capital_share - 1), 0, -self.capital_share, -1],\n",
+    "            [1, -self.gamma, (self.gamma - 1), 0, 0, 0, 0],\n",
+    "            [0, 0, 0, 0, 0, 1, 0],\n",
+    "            [0, 0, 0, -steady_state[3], -steady_state[4], 0, 0],\n",
+    "            [0, 0, 0, 0, 0, 0, 1],\n",
+    "        ])\n",
+    "        \n",
+    "        return A\n",
+    "\n",
+    "    def B_analytic(self):\n",
+    "        \n",
+    "        B = np.array([\n",
+    "            [0, 0, 0, 0, 0, 0, 0],\n",
+    "            [0, 1, 0, 0, 0, 0, 0],\n",
+    "            [0, 0, 0, 0, 0, 0, 0],\n",
+    "            [0, 0, 0, 0, 0, 0, 0],\n",
+    "            [0, 0, self.depreciation_rate, 0, 0, 1 - self.depreciation_rate, 0],\n",
+    "            [0, 0, 0, 0, 0, 0, 0],\n",
+    "            [0, 0, 0, 0, 0, 0, self.technology_shock_persistence],\n",
+    "        ])\n",
+    "        \n",
+    "        return B\n",
+    "\n",
+    "mod5 = RBC5(parameters['value'])\n",
+    "\n",
+    "display(pd.DataFrame(mod5.A_analytic(), index=equation_names, columns=lead_variable_symbols))\n",
+    "assert(np.all(np.abs(mod5.A_numeric() - mod5.A_analytic()) < 1e-10))\n",
+    "\n",
+    "display(pd.DataFrame(mod5.B_analytic(), index=equation_names, columns=lead_variable_symbols))\n",
+    "assert(np.all(np.abs(mod5.B_numeric() - mod5.B_analytic()) < 1e-10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## System reduction\n",
+    "\n",
+    "The system currently has 7 equations in 7 unknowns. This can be reduced into a system of 3 equations in 3 unknowns (consumption, capital, and the technology shock), by substituting out output, investment, labor, and leisure. Given a solution to the reduced system, the remaining four unknowns can be calculated. Also, we remove the technology shock transition from the state vector and include it instead in the stochastic shocks component $(v_{t+1})$. (Jumpers: I may explain here more?. Blanchard Kahn)\n",
+    "\n",
+    "First, notice that (using the static first order condition, production function, aggregate resource constraint, and labor-leisure tradeoff equations, respectively):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\tilde n_t & = - \\frac{1}{\\alpha} \\tilde c_t + \\frac{1}{\\alpha} \\tilde z_t + \\tilde k_t \\\\\n",
+    "\\tilde y_t & = \\tilde z_t + \\alpha \\tilde k_t + (1 - \\alpha) \\left [ - \\frac{1}{\\alpha} \\tilde c_t + \\frac{1}{\\alpha} \\tilde z_t + \\tilde k_t \\right ] \\\\\n",
+    "& = \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\\\\n",
+    "\\tilde i_t & = \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t - \\gamma \\tilde c_t \\right ] \\\\\n",
+    "& = \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha + \\gamma \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\right ] \\\\\n",
+    "\\tilde l_t & = - \\frac{\\bar n}{1 - \\bar n} \\tilde n_t\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "then we can plug these values into the remaining three equations (Euler, capital accumulation, and shock transition equations):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "k_{t+1} & = (1 - \\delta) \\tilde k_t + \\delta \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha + \\gamma \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\right ] \\\\\n",
+    "& = \\underbrace{\\left [ 1 + \\delta \\left ( \\frac{\\gamma}{1 - \\gamma} \\right ) \\right ]}_{B_{11}, F_{11}} \\tilde k_t + \\underbrace{\\frac{-\\delta (1 - \\alpha + \\gamma \\alpha)}{\\alpha (1 - \\gamma)}}_{B_{12}, F_{12}} \\tilde c_t + \\underbrace{\\frac{\\delta}{\\alpha (1 - \\gamma)}}_{C_1, G_{12}} \\tilde z_t\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "E_t \\tilde c_{t+1} & = \\tilde c_t + \\zeta (\\alpha - 1) E_t k_{t+1} + \\zeta (1 - \\alpha) E_t \\left [ - \\frac{1}{\\alpha} \\tilde c_{t+1} + \\frac{1}{\\alpha} \\tilde z_{t+1} + \\tilde k_{t+1} \\right ] + \\zeta E_t \\tilde z_{t+1} \\\\\n",
+    "& = \\tilde c_t + \\zeta (\\alpha - 1) E_t \\tilde k_{t+1} - \\zeta \\frac{(1 - \\alpha)}{\\alpha} E_t \\tilde c_{t+1} + \\zeta \\frac{(1 - \\alpha)}{\\alpha} E_t z_{t+1} + \\zeta (1 - \\alpha) E_t \\tilde k_{t+1} + \\zeta E_t \\tilde z_{t+1} \\\\\n",
+    "& = \\underbrace{\\frac{\\alpha}{\\zeta + \\alpha (1 - \\zeta)}}_{B_{22}} \\tilde c_t + \\underbrace{\\frac{\\zeta \\rho}{\\zeta + \\alpha (1 - \\zeta )}}_{C_2} \\tilde z_t\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t\n",
+    "\\end{align}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the names of the equations, variables, and parameters\n",
+    "# in the reduced model\n",
+    "reduced_equation_names = [\n",
+    "    'euler equation', 'capital accumulation'\n",
+    "]\n",
+    "reduced_variable_names = [\n",
+    "    'consumption', 'capital'\n",
+    "]\n",
+    "reduced_parameter_names = parameter_names\n",
+    "\n",
+    "# Save some symbolic forms for pretty-printing\n",
+    "reduced_variable_symbols = [\n",
+    "    r\"c\", r\"k\"\n",
+    "]\n",
+    "reduced_contemporaneous_variable_symbols = [\n",
+    "    r\"$%s_t$\" % symbol for symbol in reduced_variable_symbols\n",
+    "]\n",
+    "reduced_lead_variable_symbols = [\n",
+    "    r\"$%s_{t+1}$\" % symbol for symbol in reduced_variable_symbols\n",
+    "]\n",
+    "\n",
+    "reduced_parameter_symbols = parameter_symbols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>consumption</th>\n",
+       "      <th>capital</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>steady state</th>\n",
+       "      <td>0.506</td>\n",
+       "      <td>2.65</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              consumption  capital\n",
+       "steady state        0.506     2.65"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$c_{t+1}$</th>\n",
+       "      <th>$k_{t+1}$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      $c_{t+1}$  $k_{t+1}$\n",
+       "euler equation                1          0\n",
+       "capital accumulation          0          1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$c_t$</th>\n",
+       "      <th>$k_t$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>1.19</td>\n",
+       "      <td>-0.574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.884</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      $c_t$  $k_t$\n",
+       "euler equation         1.19 -0.574\n",
+       "capital accumulation      0  0.884"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$z_t$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>euler equation</th>\n",
+       "      <td>0.599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital accumulation</th>\n",
+       "      <td>0.154</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      $z_t$\n",
+       "euler equation        0.599\n",
+       "capital accumulation  0.154"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class ReducedRBC1(RBC5):\n",
+    "    def __init__(self, params=None):\n",
+    "        # Model dimensions\n",
+    "        self.k_params = 6\n",
+    "        self.k_variables = 2\n",
+    "        \n",
+    "        # Index for the reduced form variables\n",
+    "        self.reduced_idx = [1, -2]\n",
+    "        \n",
+    "        # Initialize parameters\n",
+    "        if params is not None:\n",
+    "            self.update(params)\n",
+    "\n",
+    "    def steady_state_numeric(self):\n",
+    "        return super(ReducedRBC1, self).steady_state_numeric()[self.reduced_idx]\n",
+    "        \n",
+    "    def steady_state_analytic(self):\n",
+    "        return super(ReducedRBC1, self).steady_state_analytic()[self.reduced_idx]\n",
+    "    \n",
+    "    def A(self):\n",
+    "        return np.eye(self.k_variables)\n",
+    "    \n",
+    "    def B(self):\n",
+    "        B11 = 1 + self.depreciation_rate * (self.gamma / (1 - self.gamma))\n",
+    "        B12 = (\n",
+    "            -self.depreciation_rate *\n",
+    "            (1 - self.capital_share + self.gamma * self.capital_share) /\n",
+    "            (self.capital_share * (1 - self.gamma))\n",
+    "        )\n",
+    "        B21 = 0\n",
+    "        B22 = self.capital_share / (self.zeta + self.capital_share*(1 - self.zeta))\n",
+    "        \n",
+    "        return np.array([[B11, B12],\n",
+    "                         [B21, B22]])\n",
+    "        \n",
+    "    def C(self):\n",
+    "        C1 = self.depreciation_rate / (self.capital_share * (1 - self.gamma))\n",
+    "        C2 = (\n",
+    "            self.zeta * self.technology_shock_persistence /\n",
+    "            (self.zeta + self.capital_share*(1 - self.zeta))\n",
+    "        )\n",
+    "        return np.array([C1, C2])[:,np.newaxis]\n",
+    "        \n",
+    "# Setup the model\n",
+    "reduced_mod1 = ReducedRBC1(parameters['value'])\n",
+    "\n",
+    "# Display the steady-states\n",
+    "reduced_steady_state = pd.DataFrame({\n",
+    "    'steady state': reduced_mod1.steady_state_analytic()\n",
+    "}, index=reduced_variable_names)\n",
+    "display(reduced_steady_state.T)\n",
+    "\n",
+    "# Test the model coefficients for accuracy\n",
+    "# (results from \"Emsm\", downloaded from\n",
+    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
+    "# on 06/19/2015)\n",
+    "assert_allclose(\n",
+    "    reduced_mod1.B(),\n",
+    "    np.array([[1.1906433, -0.57400910],\n",
+    "              [0,          0.88408644]])\n",
+    ")\n",
+    "assert_allclose(\n",
+    "    reduced_mod1.C(),\n",
+    "    np.array([[0.59900910], [0.15394769]])\n",
+    ")\n",
+    "\n",
+    "# Display the linear model coefficients\n",
+    "display(pd.DataFrame(reduced_mod1.A(), index=reduced_equation_names, columns=reduced_lead_variable_symbols))\n",
+    "display(pd.DataFrame(reduced_mod1.B(), index=reduced_equation_names, columns=reduced_contemporaneous_variable_symbols))\n",
+    "display(pd.DataFrame(reduced_mod1.C(), index=reduced_equation_names, columns=[r'$z_t$']))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving the model\n",
+    "\n",
+    "This model can be solved using the method of Blanchard-Kahn (1980). First we put it in the required form:\n",
+    "\n",
+    "$$\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde k_{t+1} \\\\\n",
+    "E_t \\tilde c_{t+1}\n",
+    "\\end{bmatrix} = \\begin{bmatrix}\n",
+    "B_{11} & B_{12} \\\\\n",
+    "B_{21} & B_{22}\n",
+    "\\end{bmatrix} \\begin{bmatrix}\n",
+    "\\tilde k_{t} \\\\\n",
+    "\\tilde c_{t} \\\\\n",
+    "\\end{bmatrix} + \\begin{bmatrix}\n",
+    "C_{1} \\\\\n",
+    "C_{2}\n",
+    "\\end{bmatrix} \\tilde z_t\n",
+    "$$\n",
+    "\n",
+    "and the solution yields the following equilibrium dynamics:\n",
+    "\n",
+    "$$\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde k_{t+1} \\\\\n",
+    "\\tilde c_{t+1}\n",
+    "\\end{bmatrix} = \\begin{bmatrix}\n",
+    "F_{11} & F_{12} \\\\\n",
+    "F_{21} & F_{22}\n",
+    "\\end{bmatrix} \\begin{bmatrix}\n",
+    "\\tilde k_{t} \\\\\n",
+    "\\tilde c_{t}\n",
+    "\\end{bmatrix} + \\begin{bmatrix}\n",
+    "G_{11} & G_{12} \\\\\n",
+    "G_{21} & G_{22}\n",
+    "\\end{bmatrix} \\begin{bmatrix}\n",
+    "\\tilde z_{t+1} \\\\\n",
+    "\\tilde z_t\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "in particular, we will find a policy function for the control variable, where the optimal choice for $\\tilde c_t$ depends only on the contemporaneous state variable $\\tilde k_t$ and exogenous shock $\\tilde z_t$. Then the policy expression can be substituted in to the reduced form capital accumulation equation, providing the evolution of the state variable in terms only of itself and the exogenous shock. The evolution of the exogenous shock is an $AR(1)$ as described above.\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\tilde c_t & = \\phi_{ck} \\tilde k_t + \\phi_{cz} \\tilde z_t \\\\\n",
+    "\\tilde k_{t+1} & = \\underbrace{\\left [ F_{11} + F_{12} \\phi_{ck} \\right ]}_{T_{kk}} \\tilde k_t + \\underbrace{\\left [G_{12} + F_{12} \\phi_{cz} \\right ]}_{T_{kz}} \\tilde z_t \\\\\n",
+    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t \\\\\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "where $F_{11}, F_{12},$ and $G_{12}$ are defined above (and we have $G_{11} = 0$), and $\\phi_{ck}, \\phi_{cz}$ are generated by the solution method. $F_{21}, F_{22}, G_{21}, G_{22}$ will not be required for estimation, but can be found by forwarding the equation for $c_t$ and plugging in for $k_{t+1}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some solution details\n",
+    "Write the Jordan decomposition of $\\mathbf{B}$ as:\n",
+    "\n",
+    "$$\\mathbf{B} = \\Lambda^{-1} J \\Lambda$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$\n",
+    "\\Lambda = \\begin{bmatrix}\n",
+    "\\Lambda_{11} & \\Lambda_{12} \\\\\n",
+    "\\Lambda_{21} & \\Lambda_{22} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\qquad \n",
+    "J = \\begin{bmatrix}\n",
+    "J_1 & 0 \\\\\n",
+    "0 & J_2 \\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "and we assume that the eigenvalues have been re-ordered so that the explosive ones are contained in $J_2$. Note that to solve the system using the Blanchard-Kahn method, we need to separate the variables into predetermined and non-predetermined. For a unique solution to exist, it must be that the number of non-predetermined variables is equal to the number of explosive eigenvalues (so that we have \"saddle-path\" stability).\n",
+    "\n",
+    "Here, $k_{t+1}$ is predetermined, and $c_{t+1}$ is non-predetermined, so we have a unique solution if there is one explosive eigenvalue and one non-explosive eigenvalue.\n",
+    "\n",
+    "Finally we can express the coefficients in the policy function above, as (see for example DeJong and Dave, section 4.3):\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\phi_{ck} & = - \\Lambda_{22}^{-1} \\Lambda_{21}\\\\\n",
+    "\\phi_{cz} & = - \\Lambda_{22}^{-1} J_2^{-1} \\left (I - \\rho J_2^{-1} D_2 \\right )^{-1} \\\\\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "where $D_2$ is the lower component of $D = \\Lambda \\mathbf{C}$. Here, $D_2 = \\Lambda_{21} C_1 + \\Lambda_{22} C_2$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ordered_jordan_decomposition(matrix):\n",
+    "    # Get eigenvalues and left eigenvectors of `matrix`\n",
+    "    # Note that the left eigenvectors of `matrix`\n",
+    "    # are the transpose of the right eigenvectors of the\n",
+    "    # transpose of matrix, and that `matrix` and `matrix'`\n",
+    "    # have the same eigenvalues\n",
+    "    eigenvalues, right_eigenvectors = np.linalg.eig(matrix.transpose())\n",
+    "    left_eigenvectors = right_eigenvectors.transpose()\n",
+    "    \n",
+    "    # Sort in increasing order\n",
+    "    idx = np.argsort(eigenvalues)\n",
+    "    \n",
+    "    # Return eigenvector matrix, diagonal eigenvalue matrix\n",
+    "    # Note that the left eigenvectors are in the rows now,\n",
+    "    # not the columns\n",
+    "    return np.diag(eigenvalues[idx]), left_eigenvectors[idx, :]\n",
+    "\n",
+    "def solve_blanchard_kahn(B, C, rho, k_predetermined):\n",
+    "    # Perform the Jordan decomposition\n",
+    "    # this yields J, Lambda\n",
+    "    eigenvalues, left_eigenvectors = ordered_jordan_decomposition(B)\n",
+    "    left_eigenvectors = left_eigenvectors\n",
+    "\n",
+    "    # Dimensions\n",
+    "    k_variables = len(B)\n",
+    "    k_nonpredetermined = k_variables - k_predetermined\n",
+    "\n",
+    "    k_stable = len(np.where(eigenvalues.diagonal() < 1)[0])\n",
+    "    k_unstable = k_variables - k_stable\n",
+    "\n",
+    "    # Make sure we're saddle-path stable\n",
+    "    if not k_unstable == k_nonpredetermined:\n",
+    "        raise RuntimeError('Blanchard-Kahn condition not met. Unique solution does not exist')\n",
+    "\n",
+    "    # Decouple the system (this is D, above)\n",
+    "    decoupled_C = np.dot(left_eigenvectors, C)\n",
+    "\n",
+    "    # Create partition indices\n",
+    "    p1 = np.s_[:k_predetermined]\n",
+    "    p2 = np.s_[k_predetermined:]\n",
+    "\n",
+    "    p11 = np.s_[:k_predetermined, :k_predetermined]\n",
+    "    p12 = np.s_[:k_predetermined, k_predetermined:]\n",
+    "    p21 = np.s_[k_predetermined:, :k_predetermined]\n",
+    "    p22 = np.s_[k_predetermined:, k_predetermined:]\n",
+    "\n",
+    "    # Solve the explosive component (controls) in terms of the\n",
+    "    # non-explosive component (states) and shocks\n",
+    "    tmp = np.linalg.inv(left_eigenvectors[p22])\n",
+    "    \n",
+    "    # This is \\phi_{ck}, above\n",
+    "    policy_state = - np.dot(tmp, left_eigenvectors[p21])\n",
+    "    # This is \\phi_{cz}, above\n",
+    "    policy_shock = -(\n",
+    "        np.dot(tmp, 1. / eigenvalues[p22]).dot(\n",
+    "            np.linalg.inv(\n",
+    "                np.eye(k_nonpredetermined) -\n",
+    "                rho / eigenvalues[p22]\n",
+    "            )\n",
+    "        ).dot(decoupled_C[p2])\n",
+    "    )\n",
+    "\n",
+    "    # Solve for the non-explosive transition\n",
+    "    # This is T_{kk}, above\n",
+    "    transition_state = B[p11] + np.dot(B[p12], policy_state)\n",
+    "    # This is T_{kz}, above\n",
+    "    transition_shock = np.dot(B[p12], policy_shock) + C[p1]\n",
+    "    \n",
+    "    return policy_state, policy_shock, transition_state, transition_shock\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ReducedRBC2(ReducedRBC1):\n",
+    "    def solve(self, params=None):\n",
+    "        # Update the model parameters, if given\n",
+    "        if params is not None:\n",
+    "            self.update(params)\n",
+    "        \n",
+    "        # Solve the model\n",
+    "        phi_ck, phi_cz, T_kk, T_kz = solve_blanchard_kahn(\n",
+    "            self.B(), self.C(),\n",
+    "            self.technology_shock_persistence, 1\n",
+    "        )\n",
+    "        \n",
+    "        # Create the design matrix\n",
+    "        inv_capital_share = 1. / self.capital_share\n",
+    "        tmp1 = (1 - self.capital_share) * inv_capital_share\n",
+    "        phi_yk = 1 - tmp1 * phi_ck\n",
+    "        phi_yz = inv_capital_share - tmp1 * phi_cz\n",
+    "        phi_nk = 1 - inv_capital_share * phi_ck\n",
+    "        phi_nz = inv_capital_share * (1 - phi_cz)\n",
+    "        design = np.r_[\n",
+    "            phi_yk, phi_yz, phi_nk, phi_nz, phi_ck, phi_cz\n",
+    "        ].reshape((3,2))\n",
+    "        \n",
+    "        # Create the transition matrix\n",
+    "        transition = np.r_[\n",
+    "            T_kk[0,0], T_kz[0,0], 0, self.technology_shock_persistence\n",
+    "        ].reshape((2,2))\n",
+    "        \n",
+    "        return design, transition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Modulus of eigenvalues of B: [0.884 1.191]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>$\\phi_{ck}$</th>\n",
+       "      <th>$\\phi_{cz}$</th>\n",
+       "      <th>$T_{kk}$</th>\n",
+       "      <th>$T_{kz}$</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>solution</th>\n",
+       "      <td>0.534</td>\n",
+       "      <td>0.487</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>0.319</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          $\\phi_{ck}$  $\\phi_{cz}$  $T_{kk}$  $T_{kz}$\n",
+       "solution        0.534        0.487     0.884     0.319"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reduced_mod2 = ReducedRBC2(parameters['value'])\n",
+    "\n",
+    "# Check the Blanchard-Kahn conditions\n",
+    "eigenvalues, left_eigenvectors = ordered_jordan_decomposition(reduced_mod2.B())\n",
+    "print('Modulus of eigenvalues of B:', np.abs(eigenvalues.diagonal()))\n",
+    "\n",
+    "# Test the eigenvalues and eigenvectors\n",
+    "# (results from \"Emsm\", downloaded from\n",
+    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
+    "# on 06/19/2015)\n",
+    "assert_allclose(eigenvalues.diagonal(), [0.88408644, 1.1906433])\n",
+    "assert_allclose(left_eigenvectors[0,:], [0, 1])\n",
+    "# Note the second eigenvector returned by numpy\n",
+    "# is scaled differently than the one returned\n",
+    "# by Gauss, so we need to rescale it to compare.\n",
+    "assert_allclose(\n",
+    "    left_eigenvectors[1,:] / left_eigenvectors[1,1],\n",
+    "    [-0.53406267, 1]\n",
+    ")\n",
+    "\n",
+    "# Solve the model\n",
+    "design, transition = reduced_mod2.solve()\n",
+    "\n",
+    "# Test the solved model coefficients for accuracy\n",
+    "assert_allclose(design[2,:], [0.53406267, 0.48719795])\n",
+    "\n",
+    "# Display the results\n",
+    "display(pd.DataFrame(\n",
+    "    np.r_[design[2,:], transition[0,:]],\n",
+    "    columns=['solution'],\n",
+    "    index=[r'$\\phi_{ck}$', r'$\\phi_{cz}$', r'$T_{kk}$', r'$T_{kz}$']\n",
+    ").T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## State-space form\n",
+    "\n",
+    "Assuming that $\\xi_t = (\\tilde k_t, \\tilde z_t)'$ is unobservable and $s_t = (\\tilde y_t, \\tilde n_t, \\tilde c_t)'$ is observable, the above equations give use the following relationship:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde y_t \\\\\n",
+    "\\tilde n_t \\\\\n",
+    "\\tilde c_t \\\\\n",
+    "\\end{bmatrix} & = \\begin{bmatrix}\n",
+    "\\phi_{yk} & \\phi_{yz} \\\\\n",
+    "\\phi_{nk} & \\phi_{nz} \\\\\n",
+    "\\phi_{ck} & \\phi_{cz} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde k_{t} \\\\\n",
+    "\\tilde z_{t}\n",
+    "\\end{bmatrix} \\\\\n",
+    "& = \\begin{bmatrix}\n",
+    "1 - \\frac{1-\\alpha}{\\alpha} \\phi_{ck} & \\frac{1}{\\alpha} - \\frac{1-\\alpha}{\\alpha} \\phi_{cz} \\\\\n",
+    "1 - \\frac{1}{\\alpha} \\phi_{ck} & \\frac{1}{\\alpha} \\left [ 1 - \\phi_{cz} \\right ] \\\\\n",
+    "\\phi_{ck} & \\phi_{cz} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde k_{t} \\\\\n",
+    "\\tilde z_{t}\n",
+    "\\end{bmatrix}\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "The above is the observation equation. In order to overcome the stochastic singularity problem, we augment with measurement errors $u_t = (u_{yt}, u_{nt}, u_{ct})'$:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "s_t & = \\mathbf{\\Phi} \\xi_t + u_t\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "And the transition equation is:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde k_{t+1} \\\\\n",
+    "\\tilde z_{t+1} \\\\\n",
+    "\\end{bmatrix} & = \\begin{bmatrix}\n",
+    "T_{kk} & T_{kz}\\\\\n",
+    "0 & \\rho \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\begin{bmatrix}\n",
+    "\\tilde k_{t} \\\\\n",
+    "\\tilde z_{t}\n",
+    "\\end{bmatrix} + \\begin{bmatrix}\n",
+    "0 \\\\\n",
+    "1\n",
+    "\\end{bmatrix}\n",
+    "\\varepsilon_t\n",
+    "\\end{align}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Python package `statsmodels` has support for maximum likelihood analysis of state space models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calibration (Prescott 1986)\n",
+    "\n",
+    "This problem is not solvable by hand in general. The basic idea, proposed by Kydland and Prescott (1982) is the following:\n",
+    "Pick parameters of the model to match some key long-run macro facts or microeconomic evidence. Simulate the model and generate artificial data. Finally, compare the moments of the artificial daata with the real data (not the\n",
+    "same moments you used to calibrate).\n",
+    "\n",
+    "\n",
+    "There are six parameters\n",
+    "\n",
+    "$$\\{ \\beta, \\psi, \\delta, \\alpha, \\rho, \\sigma^2 \\}$$\n",
+    "\n",
+    "and of those three will be calibrated: $\\{ \\psi = 3, \\delta = 0.10, \\alpha = 0.36 \\}$ (note that $\\psi$ only influences the steady-state labor supply and so does not enter into the state-space form parameters). \n",
+    "The remaining three parameters, $\\{ \\beta, \\rho, \\sigma^2 \\}$, will be estimated by maximum likelhood.\n",
+    "\n",
+    "As outlined in the notes, the standard deviation of labor input generated is much less than the standard deviation from the US data.\n",
+    "\n",
+    "| |$\\sigma_a$|$\\sigma_y$|$\\sigma_l$  \t\n",
+    "|---\t|---\t|---\t\n",
+    "| U.S   |   0.76\t|1.76 |1.67  \t\n",
+    "| Model |   0.76    |1.48 |0.76  \t \t\n",
+    "\n",
+    "\n",
+    "Since the first column is calibrated, it isn’t a test of the model. The second column\n",
+    "comes out of the model, and isn’t too bad a fit. However, the third column is a\n",
+    "terrible fit. What it says is that labor input is much more variable over the course of\n",
+    "the business cycle than this model would suggest.\n",
+    "What’s going on? To understand the answer, we need to understand why hours\n",
+    "fluctuate in this model at all. Recall that we deliberately constructed the model (by\n",
+    "choosing a utility function that was Cobb-Douglas in consumption and leisure) in a\n",
+    "way designed to prohibit any long-run response of hours worked to wages. Since hours\n",
+    "worked are being chosen freely on a day-by-day basis by workers in this model, there\n",
+    "must be some incentive that causes them to be willing to put up with short-term\n",
+    "variation in hours (over the business cycle).\n",
+    "The answer is that transitory productivity shocks provide an incentive to work\n",
+    "harder some times than others. In particular, if there is a temporary positive productivity\n",
+    "shock you will be willing to work longer hours than usual, while if there is\n",
+    "a negative productivity shock everybody wants to take a vacation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "We will evaluate the model using two datasets. The first will be simulated, allowing us to explore the capacity of the model to recover the true parameters. The second will estimate the model on observed economic data. Then, as Prescott undelines, we ll compare the standard deviation of output, labor and productivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulated dataset\n",
+    "\n",
+    "Reference: Ruge-Murcia, Francisco J. 2007.\n",
+    "    \"Methods to Estimate Dynamic Stochastic General Equilibrium Models.\"\n",
+    "    Journal of Economic Dynamics and Control 31 (8): 2599–2636."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(12345)\n",
+    "\n",
+    "# Parameters\n",
+    "T = 200   # number of periods to simulate\n",
+    "T0 = 100  # number of initial periods to \"burn\"\n",
+    "\n",
+    "# We can use the exact random draws from \"Emsm\", downloaded from\n",
+    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
+    "# on 06/19/2015)\n",
+    "rm2007_eps = [0.0089954547, 0.069601997, -0.0081704445, -0.036704078, -0.026966673, -0.013741121, 0.0089339760, -0.0056557030, -0.0073353523, 0.027214134, 0.0036223219, -0.033331014, 0.032539993, 0.044695276, 0.012599442, -0.020012497, -0.065070833, 0.024777248, -0.058297234, -0.072139533, 0.080062379, 0.023164655, -0.028318809, 0.023734384, -0.023575740, 0.058697373, -0.00080918191, 0.029482310, 0.059178715, -0.010752551, 0.049127695, 0.063137227, -0.015733529, 0.018006224, 0.051256459, -0.014467873, 0.042611930, -0.078176552, -0.0040812905, -0.0086694118, 0.016261678, 0.0055330257, 0.026286130, -0.0066732973, 0.019133914, 0.018442169, 0.0046151171, 0.0015229921, 0.047776839, -0.058401266, 0.014895019, -0.0070732464, -0.036637349, 0.018778403, 0.0030934044, -0.033385312, -0.0044036385, -0.0029289904, -0.029415234, -0.010308393, -0.023496361, -0.023784028, 0.045396730, -0.021532569, -0.086991302, 0.046579589, 0.015086674, 0.0054060766, 0.0094114004, 0.014372645, -0.060998265, -0.0047493261, -0.030991307, -0.022061370, -0.020225482, -0.013470628, -0.013967446, -0.021552474, -0.054801903, -0.0052111107, 0.0080784668, 0.042868645, -0.0015220824, -0.061354829, 0.053529145, -0.020002403, -0.00053686088, 0.085988265, 0.037919020, 0.023531373, 0.0046336046, 0.012880821, 0.0037651140, -0.059647623, -0.027420909, -0.063257854, -0.010324261, -0.025627797, -0.017646345, -0.00091871809, 0.0066086013, 0.0018793222, 0.019543168, -0.031823750, -0.0092249652, 0.013246704, 0.014181125, 0.047271352, 0.047259268, 0.010107337, -0.083925083, -0.036031657, -0.0022387325, -0.035090684, -0.022218572, -0.017554625, 0.033953597, 0.010744674, -0.010891498, -0.0035293110, -0.033522281, -0.072168448, -0.0042416089, -0.025190520, 0.11066349, 0.029308577, -0.018047271, 0.055748729, -0.0016904632, -0.035578602, -0.10830804, -0.013671301, -0.010389470, -0.012295055, 0.055696357, 0.020597878, 0.026447061, -0.054887926, -0.045563156, 0.060229793, 0.028380999, -0.0034341303, 0.038103203, 0.012224323, 0.016752740, -0.0065436404, -0.0010711498, -0.025486203, -0.055621838, 0.0096008728, -0.088779172, 0.092452909, 0.057714587, -0.0057425132, 0.023627700, -0.029821882, -0.012037717, -0.074682148, -0.062682990, -0.038800349, -0.094946077, 0.074545642, -0.00050272713, -0.0075839744, -0.037362343, 0.012332294, 0.10490393, 0.049997520, 0.033916235, -0.061734224, -0.015363425, 0.057711167, -0.051687840, 0.031219589, 0.041031894, 0.0051038726, -0.013144180, 0.054156433, -0.0090438895, 0.023331707, -0.0079434321, -0.0029084658, -0.0064262300, 0.044577448, 0.014816901, 0.043276307, -0.011412684, -0.0026201902, -0.021138420, -0.0020795206, -0.042017897, -0.028148295, 0.063945871, -0.049724502, -0.048571001, -0.061207381, 0.050007129, 0.0062884061, 0.057948665, -0.012780170, -0.020464058, 0.023577863, 0.030007840, -0.013682281, 0.044281158, 0.033864209, -0.016235593, 0.0052712906, 0.035426922, -0.084935662, -0.061241657, 0.038759520, 0.019838792, -0.038971482, -0.043112193, -0.10098203, 0.011744644, 0.014708720, 0.035224935, 0.0098378679, 0.031205446, 0.026015597, -0.048897576, -0.042539822, -0.036330332, -0.033689415, 0.029665808, 0.0086127051, 0.038663112, -0.064534479, -0.036174560, -0.034225451, -0.0084848888, -0.011724560, -0.037544322, -0.013054490, -0.062983798, 0.011448707, 0.0022791918, -0.054508196, 0.046134801, -0.063884585, 0.048918326, 0.018358644, -0.011278321, 0.021175611, -0.0069196463, -0.084987826, 0.016286265, -0.031783692, -0.041129528, -0.11686860, 0.0040626993, 0.057649830, 0.019174675, -0.010319778, 0.080549326, -0.058124228, -0.027757539, -0.0028474062, 0.012399938, -0.088780901, 0.077048657, 0.070548177, -0.023784957, 0.035935388, 0.064960358, 0.019987594, 0.062245578, 0.0014217956, 0.057173164, 0.043800495, -0.023484057, 0.021398628, -0.012723988, 0.012587101, -0.049855702, 0.070557277, -0.017640273, -0.031555592, -0.030900124, -0.028508626, -0.029129143, 0.0024196883, -0.026937200, -0.011642554, -0.045071194, -0.013049519, -0.021908382, 0.017900266, -0.019798107, -0.040774046, -0.027013698, 0.065691125, 0.0081570086, -0.012601818, 0.017918061, 0.017225503, 0.0021227212, 0.032141622, 0]\n",
+    "# Or we can draw our own\n",
+    "gen_eps = np.random.normal(0, reduced_mod1.technology_shock_std, size=(T+T0+1))\n",
+    "\n",
+    "eps = rm2007_eps\n",
+    "\n",
+    "# Create and solve the model\n",
+    "reduced_mod2 = ReducedRBC2(parameters['value'])\n",
+    "design, transition = reduced_mod2.solve()\n",
+    "selection = np.array([0, 1])\n",
+    "\n",
+    "# Generate variables\n",
+    "raw_observed = np.zeros((T+T0+1,3))\n",
+    "raw_state = np.zeros((T+T0+2,2))\n",
+    "\n",
+    "for t in range(T+T0+1):\n",
+    "    raw_observed[t] = np.dot(design, raw_state[t])\n",
+    "    raw_state[t+1] = np.dot(transition, raw_state[t]) + selection * eps[t]\n",
+    "\n",
+    "# Test that our simulated series are the same as in \"Emsm\"\n",
+    "# Note: Gauss uses ddof=1 for std dev calculation\n",
+    "assert_allclose(np.mean(raw_state[1:-1,:], axis=0), [-0.0348286036, -0.0133121934])\n",
+    "assert_allclose(np.std(raw_state[1:-1,:], axis=0, ddof=1), [0.122766006, 0.0742206044])\n",
+    "assert_allclose(np.mean(raw_observed[1:,:], axis=0), [-0.027208998, -0.0021226675, -0.025086330])\n",
+    "assert_allclose(np.std(raw_observed[1:,:], axis=0, ddof=1), [0.14527028, 0.089694148, 0.090115364])\n",
+    "\n",
+    "# Drop the first 100 observations\n",
+    "sim_observed = raw_observed[T0+1:,:]\n",
+    "sim_state = raw_state[T0+1:-1,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(13,4))\n",
+    "\n",
+    "ax.plot(sim_observed[:,0], label='Output')\n",
+    "ax.plot(sim_observed[:,1], label='Labor')\n",
+    "ax.plot(sim_observed[:,2], label='Consumption')\n",
+    "\n",
+    "ax.set_title('Simulated observed series')\n",
+    "ax.xaxis.grid()\n",
+    "ax.legend(loc='lower left')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(13,4))\n",
+    "\n",
+    "ax.plot(sim_state[:,0], label='Capital')\n",
+    "ax.plot(sim_state[:,1], label='Technology shock')\n",
+    "\n",
+    "ax.set_title('Simulated unobserved states')\n",
+    "ax.xaxis.grid()\n",
+    "ax.legend(loc='lower left')\n",
+    "\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Observed economic data\n",
+    "\n",
+    "We use data on hours worked, consumption, and investment. This is the most useful bit for interpretation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get some FRED data\n",
+    "start='1984-01'\n",
+    "end = '2015-01'\n",
+    "labor = DataReader('HOANBS', 'fred', start=start, end=end)        # hours\n",
+    "consumption = DataReader('PCECC96', 'fred', start=start, end=end) # billions of dollars\n",
+    "investment = DataReader('GPDI', 'fred', start=start, end=end)     # billions of dollars\n",
+    "population = DataReader('CNP16OV', 'fred', start=start, end=end)  # thousands of persons\n",
+    "recessions = DataReader('USRECQ', 'fred', start=start, end=end)\n",
+    "\n",
+    "# Collect the raw values\n",
+    "raw = pd.concat((labor, consumption, investment, population.resample('QS').mean()), axis=1)\n",
+    "raw.columns = ['labor', 'consumption', 'investment', 'population']\n",
+    "raw['output'] = raw['consumption'] + raw['investment']\n",
+    "\n",
+    "# Make the data consistent with the model\n",
+    "y = np.log(raw.output * 10**(9-3) / raw.population)\n",
+    "n = np.log(raw.labor * (1e3 * 40) / raw.population)\n",
+    "c = np.log(raw.consumption * 10**(9-3) / raw.population)\n",
+    "\n",
+    "# Make the data stationary\n",
+    "y = y.diff()[1:]\n",
+    "n = n.diff()[1:]\n",
+    "c = c.diff()[1:]\n",
+    "\n",
+    "# Construct the final dataset\n",
+    "econ_observed = pd.concat((y, n, c), axis=1)\n",
+    "econ_observed.columns = ['output','labor','consumption']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(13,4))\n",
+    "\n",
+    "dates = econ_observed.index._mpl_repr()\n",
+    "\n",
+    "ax.plot(dates, econ_observed.output, label='Output')\n",
+    "ax.plot(dates, econ_observed.labor, label='Labor')\n",
+    "ax.plot(dates, econ_observed.consumption, label='Consumption')\n",
+    "\n",
+    "rec = recessions.resample('QS').last().loc[econ_observed.index[0]:].iloc[:, 0].values\n",
+    "ylim = ax.get_ylim()\n",
+    "ax.fill_between(dates, ylim[0]+1e-5, ylim[1]-1e-5, rec, facecolor='k', alpha=0.1)\n",
+    "\n",
+    "ax.xaxis.grid()\n",
+    "ax.legend(loc='lower left');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EstimateRBC1(sm.tsa.statespace.MLEModel):\n",
+    "    def __init__(self, output=None, labor=None, consumption=None,\n",
+    "                 measurement_errors=True,\n",
+    "                 disutility_labor=3, depreciation_rate=0.025,\n",
+    "                 capital_share=0.36, **kwargs):\n",
+    "\n",
+    "        # Determine provided observed variables\n",
+    "        self.output = output is not None\n",
+    "        self.labor = labor is not None\n",
+    "        self.consumption = consumption is not None\n",
+    "        self.observed_mask = (\n",
+    "            np.array([self.output, self.labor, self.consumption], dtype=bool)\n",
+    "        )\n",
+    "        \n",
+    "        observed_variables = np.r_[['output', 'labor', 'consumption']]\n",
+    "        self.observed_variables = observed_variables[self.observed_mask]\n",
+    "        \n",
+    "        self.measurement_errors = measurement_errors\n",
+    "        \n",
+    "        # Construct the full endogenous array\n",
+    "        endog = []\n",
+    "        if self.output:\n",
+    "            endog.append(np.array(output))\n",
+    "        if self.labor:\n",
+    "            endog.append(np.array(labor))\n",
+    "        if self.consumption:\n",
+    "            endog.append(np.array(consumption))\n",
+    "        endog = np.c_[endog].transpose()\n",
+    "        \n",
+    "        # Initialize the statespace model\n",
+    "        super(EstimateRBC1, self).__init__(endog, k_states=2, k_posdef=1, **kwargs)\n",
+    "        self.initialize_stationary()\n",
+    "        self.data.ynames = self.observed_variables\n",
+    "        \n",
+    "        # Check for stochastic singularity\n",
+    "        if self.k_endog > 1 and not measurement_errors:\n",
+    "            raise ValueError('Stochastic singularity encountered')\n",
+    "        \n",
+    "        # Save the calibrated parameters\n",
+    "        self.disutility_labor = disutility_labor\n",
+    "        self.depreciation_rate = depreciation_rate\n",
+    "        self.capital_share = capital_share\n",
+    "        \n",
+    "        # Create the structural model\n",
+    "        self.structural = ReducedRBC2()\n",
+    "        \n",
+    "        # Setup fixed elements of the statespace matrices\n",
+    "        self['selection', 1, 0] = 1\n",
+    "        \n",
+    "        idx = np.diag_indices(self.k_endog)\n",
+    "        self._idx_obs_cov = ('obs_cov', idx[0], idx[1])\n",
+    "        \n",
+    "    @property\n",
+    "    def start_params(self):\n",
+    "        start_params = [0.99, 0.5, 0.01]\n",
+    "        if self.measurement_errors:\n",
+    "            start_meas_error = np.r_[[0.1]*3]\n",
+    "            start_params += start_meas_error[self.observed_mask].tolist()\n",
+    "        \n",
+    "        return start_params\n",
+    "\n",
+    "    @property\n",
+    "    def param_names(self):\n",
+    "        param_names = ['beta', 'rho', 'sigma.vareps']\n",
+    "        if self.measurement_errors:\n",
+    "            meas_error_names = np.r_[['sigma2.y', 'sigma2.n', 'sigma2.c']]\n",
+    "            param_names += meas_error_names[self.observed_mask].tolist()\n",
+    "        \n",
+    "        return param_names\n",
+    "    \n",
+    "    def transform_params(self, unconstrained):\n",
+    "        constrained = np.zeros(unconstrained.shape, unconstrained.dtype)\n",
+    "        \n",
+    "        # Discount rate is between 0 and 1\n",
+    "        constrained[0] = max(1 / (1 + np.exp(unconstrained[0])) - 1e-4, 1e-4)\n",
+    "        # Technology shock persistence is between -1 and 1\n",
+    "        constrained[1] = unconstrained[1] / (1 + np.abs(unconstrained[1]))\n",
+    "        # Technology shock std. dev. is positive\n",
+    "        constrained[2] = np.abs(unconstrained[2])\n",
+    "        # Measurement error variances must be positive\n",
+    "        if self.measurement_errors:\n",
+    "            constrained[3:3+self.k_endog] = unconstrained[3:3+self.k_endog]**2\n",
+    "        \n",
+    "        return constrained\n",
+    "    \n",
+    "    def untransform_params(self, constrained):\n",
+    "        unconstrained = np.zeros(constrained.shape, constrained.dtype)\n",
+    "        \n",
+    "        # Discount rate is between 0 and 1\n",
+    "        unconstrained[0] = np.log((1 - constrained[0] + 1e-4) / (constrained[0] + 1e-4))\n",
+    "        # Technology shock persistence is between -1 and 1\n",
+    "        unconstrained[1] = constrained[1] / (1 + constrained[1])\n",
+    "        # Technology shock std. dev. is positive\n",
+    "        unconstrained[2] = constrained[2]\n",
+    "        # Measurement error variances must be positive\n",
+    "        if self.measurement_errors:\n",
+    "            unconstrained[3:3+self.k_endog] = constrained[3:3+self.k_endog]**0.5\n",
+    "        \n",
+    "        return unconstrained\n",
+    "    \n",
+    "    def update(self, params, **kwargs):\n",
+    "        params = super(EstimateRBC1, self).update(params, **kwargs)\n",
+    "        \n",
+    "        # Get the parameters of the structural model\n",
+    "        # Note: we are calibrating three parameters\n",
+    "        structural_params = np.r_[\n",
+    "            params[0],\n",
+    "            self.disutility_labor,\n",
+    "            self.depreciation_rate,\n",
+    "            self.capital_share,\n",
+    "            params[1:3]\n",
+    "        ]\n",
+    "        \n",
+    "        # Solve the model\n",
+    "        design, transition = self.structural.solve(structural_params)\n",
+    "        \n",
+    "        # Update the statespace representation\n",
+    "        self['design'] = design[self.observed_mask, :]\n",
+    "        if self.measurement_errors:\n",
+    "            self[self._idx_obs_cov] = params[3:3+self.k_endog]\n",
+    "        self['transition'] = transition\n",
+    "        self['state_cov', 0, 0] = self.structural.technology_shock_std**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimation on simulated data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                  Statespace Model Results                                  \n",
+      "============================================================================================\n",
+      "Dep. Variable:     ['output' 'labor' 'consumption']   No. Observations:                  200\n",
+      "Model:                                 EstimateRBC1   Log Likelihood                3236.232\n",
+      "Date:                              Tue, 18 Nov 2025   AIC                          -6460.465\n",
+      "Time:                                      21:39:03   BIC                          -6440.675\n",
+      "Sample:                                           0   HQIC                         -6452.456\n",
+      "                                              - 200                                         \n",
+      "Covariance Type:                                opg                                         \n",
+      "================================================================================\n",
+      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "--------------------------------------------------------------------------------\n",
+      "beta             0.9500   1.03e-05    9.2e+04      0.000       0.950       0.950\n",
+      "rho              0.8500   5.46e-05   1.56e+04      0.000       0.850       0.850\n",
+      "sigma.vareps     0.0468   1.02e-06   4.59e+04      0.000       0.047       0.047\n",
+      "sigma2.y      3.911e-12   1.87e-06   2.09e-06      1.000   -3.66e-06    3.66e-06\n",
+      "sigma2.n      9.284e-12   1.86e-06   4.99e-06      1.000   -3.65e-06    3.65e-06\n",
+      "sigma2.c      8.282e-05      0.000      0.500      0.617      -0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):     0.77, 35.95, 36.40   Jarque-Bera (JB):     0.09, 0.66, 0.66\n",
+      "Prob(Q):                  0.38, 0.00, 0.00   Prob(JB):             0.95, 0.72, 0.72\n",
+      "Heteroskedasticity (H):   0.86, 0.67, 0.68   Skew:               -0.05, 0.10, -0.11\n",
+      "Prob(H) (two-sided):      0.54, 0.10, 0.11   Kurtosis:             2.95, 2.79, 2.81\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+      "[2] Covariance matrix is singular or near-singular, with condition number 1.88e+19. Standard errors may be unstable.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/DemARK/lib/python3.10/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setup the statespace model\n",
+    "sim_mod = EstimateRBC1(\n",
+    "    output=sim_observed[:,0],\n",
+    "    labor=sim_observed[:,1],\n",
+    "    consumption=sim_observed[:,2],\n",
+    "    measurement_errors=True\n",
+    ")\n",
+    "\n",
+    "# sim_res = sim_mod.fit(maxiter=1000, information_matrix_type='oim')\n",
+    "sim_res = sim_mod.fit(maxiter=1000)\n",
+    "\n",
+    "print(sim_res.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Estimation on observed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/DemARK/lib/python3.10/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "/opt/anaconda3/envs/DemARK/lib/python3.10/site-packages/statsmodels/base/optimizer.py:21: FutureWarning: Keyword arguments have been passed to the optimizer that have no effect. The list of allowed keyword arguments for method lbfgs is: m, pgtol, factr, maxfun, epsilon, approx_grad, bounds, loglike_and_score, iprint. The list of unsupported keyword arguments passed include: information_matrix_type. After release 0.14, this will raise.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                  Statespace Model Results                                  \n",
+      "============================================================================================\n",
+      "Dep. Variable:     ['output' 'labor' 'consumption']   No. Observations:                  124\n",
+      "Model:                                 EstimateRBC1   Log Likelihood                1445.651\n",
+      "Date:                              Tue, 18 Nov 2025   AIC                          -2879.301\n",
+      "Time:                                      21:39:04   BIC                          -2862.380\n",
+      "Sample:                                  04-01-1984   HQIC                         -2872.427\n",
+      "                                       - 01-01-2015                                         \n",
+      "Covariance Type:                                opg                                         \n",
+      "================================================================================\n",
+      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "--------------------------------------------------------------------------------\n",
+      "beta             0.9847      0.011     91.107      0.000       0.964       1.006\n",
+      "rho              0.9561      0.026     36.697      0.000       0.905       1.007\n",
+      "sigma.vareps     0.0022      0.000      9.878      0.000       0.002       0.003\n",
+      "sigma2.y       8.63e-06   2.49e-06      3.469      0.001    3.75e-06    1.35e-05\n",
+      "sigma2.n      2.521e-05   4.89e-06      5.158      0.000    1.56e-05    3.48e-05\n",
+      "sigma2.c      1.595e-05   2.25e-06      7.080      0.000    1.15e-05    2.04e-05\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):      0.11, 22.97, 0.92   Jarque-Bera (JB):    16.90, 1.27, 3.15\n",
+      "Prob(Q):                  0.74, 0.00, 0.34   Prob(JB):             0.00, 0.53, 0.21\n",
+      "Heteroskedasticity (H):   1.68, 0.99, 0.51   Skew:                0.37, -0.23, 0.28\n",
+      "Prob(H) (two-sided):      0.10, 0.98, 0.03   Kurtosis:             4.65, 3.19, 3.55\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Setup the statespace model\n",
+    "econ_mod = EstimateRBC1(\n",
+    "    output=econ_observed['output'],\n",
+    "    labor=econ_observed['labor'],\n",
+    "    consumption=econ_observed['consumption'],\n",
+    "    measurement_errors=True,\n",
+    "    dates=econ_observed.index\n",
+    ")\n",
+    "\n",
+    "econ_res = econ_mod.fit(maxiter=1000, information_matrix_type='oim')\n",
+    "\n",
+    "print(econ_res.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observed data throws up variation in labor of nearly 5 units (Z), whereas simulated data throws up with calibrated parameters a \n",
+    "value of 0.002, in support of the observations we made earlier above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "    [1] Chris Caroll's Lecture Notes\n",
+    "    [2] DeJong, David N., and Chetan Dave. 2011.\n",
+    "        Structural Macroeconometrics. Second edition.\n",
+    "        Princeton: Princeton University Press.\n",
+    "    [3] Ruge-Murcia, Francisco J. 2007.\n",
+    "        \"Methods to Estimate Dynamic Stochastic General Equilibrium Models.\"\n",
+    "        Journal of Economic Dynamics and Control 31 (8): 2599–2636.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (DemARK)",
+   "language": "python",
+   "name": "demark"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}