# Estimating DSGE by Maximum Likelihood in Python
# Author: Michal Miktus
# Date: 20.03.2019
# Import libraries
from LinApp_Solve import LinApp_Solve
import matplotlib.pyplot as plt
import seaborn as sn
import pandas as pd
import numpy as np
import pickle
from numpy import vstack, hstack, zeros, eye
from Linear_Time_Iteration import Linear_Time_Iteration
# %matplotlib inline
# Set printing options
np.set_printoptions(precision=3, suppress=True, linewidth=120)
pd.set_option(
'float_format',
lambda x: '%.3g' % x,
)
# --------------------------------------------------------------------------
# -- Define the model
# --------------------------------------------------------------------------
# VARIABLES [M,3] cell array: Variable name (case sensitive) ~ Variable type ~ Description
# Variable type: 'X' ... Endogenous state variable
# 'Y' ... Endogenous other (jump) variable
# 'Z' ... Exogenous state variable
# 'U' ... Innovation to exogenous state variable
# '' ... Skip variable
variable_symbols = [
r'$e_a$', r'$e_I$', r'$e_b$', r'$e_L$', r'$e_G$', r'$e_pi$', r'$pi$',
