LinApp_FindSS.py

'''
Version 1.0, written by Kerk Phillips, April 2014

Adapted by Yulong Li, November 2015
'''
import numpy as np
import scipy.optimize as opt

def steady(XYbar, Zbar, funcname, param, nx, ny):
	Xbar = XYbar[0:nx]
	Ybar = XYbar[nx:nx+ny]
	Zbar = np.array(Zbar)
	if ny==0:
		In = np.concatenate((Xbar, Xbar, Xbar, Zbar, Zbar))
	else:
		In = np.concatenate((Xbar, Xbar, Xbar, Ybar, Ybar, Zbar, Zbar))
	In = np.concatenate((Xbar, Xbar, Xbar, Ybar, Ybar, Zbar, Zbar))
	Out = funcname(In, param)
	return Out


def LinApp_FindSS(funcname, param, guessXY, Zbar, nx, ny):
#	'''
#	Finds the steady state for a DSGE model numerically
#
#	Parameters
#    -----------
#    funcname: function
#    the name of the function which generates a column vector 
#    from ny+nx dynamic equations.
#		The ny equations to be linearized into the form below in the first 
#		ny rows.
#		A X(t) + B X(t-1) + C Y(t) + D Z(t) = 0 
#		The function must be written so as to evaluate to zero for all rows
#		in the steady state.
#
#	param: array, dtype=float
#		a vector of parameter values to be passed to funcname.
#	
#	guessXY: array, dtype=float
#		guess for the steady state values of X and Y
#	
#	Zbar: array, dtype=float
#		nz vector of Z steady state values
#	
#	nx: number, dtype=int
#		number of X variables
#	
#	ny: number, dtype=int
#		number of Y variables
#
#	Returns
#    --------
#	XYbar: array, dtype=float
#		1-by-(nx+ny) vector of X and Y steady state values, with the X
#		values in positions 1 - nx and the Y values in nx+1 - nx+ny.
#	'''
    f = lambda XYbar: steady(XYbar, Zbar, funcname, param, nx, ny)
    XYbar = opt.fsolve(f, guessXY)

    return XYbar