Source code for snowdrop.src.preprocessor.f_rhs

from numba import njit
    
[docs] @njit def f_rhs(x, p, exog=[0], order=0, ind=None): ### This code was generated by Python. ### Monetary policy model example # First order derivatives are employed in most of the models to compute Jacobian. # Higher order derivatives are used in nonlinear rational expectations models. from scipy.special import lambertw as LambertW from snowdrop.src.preprocessor.functions import Heaviside,Max,Min,Abs,DiracDelta from snowdrop.src.preprocessor.condition import IfThenElse,IfThen,Derivative,Subs,Positive,Negative,myzif import numpy as np from numpy import exp, sin, cos, tan, sqrt, sign, log # Initialize variables _xi_1 = 0 _xi_2 = 0 _xi_3 = 0 PDOT__p1_ = x[0] PDOT__ = x[4] RR__ = x[5] RS__ = x[6] Y__ = x[7] PDOT__m1_ = x[8] RR__m1_ = x[9] Y__m1_ = x[11] ey__ = x[12] err__ = x[13] ers__ = x[14] epdot__ = x[15] # Set parameters g = p[0] p_pdot1 = p[1] p_pdot2 = p[2] p_pdot3 = p[3] p_rs1 = p[4] p_y1 = p[5] p_y2 = p[6] p_y3 = p[7] # Set exogenous variables exo__ = exog[0] # Function: function = np.zeros(4) function[0] = epdot__ + PDOT__m1_*(1 - p_pdot1) + PDOT__p1_*p_pdot1 + p_pdot3*(-g + g**2/(-Y__m1_ + g)) + (-g + g**2/(-Y__ + g))*p_pdot2 function[1] = RS__ + err__ - PDOT__m1_*(1 - p_pdot1) - PDOT__p1_*p_pdot1 function[2] = Y__ + ers__ + exo__ + p_rs1*PDOT__ function[3] = ey__ + p_y1*Y__m1_ - p_y2*RR__ - p_y3*RR__m1_ if order == 0: return function