Source code for snowdrop.src.numeric.solver.AIM.reducedForm

# import numpy and scipy packages
import numpy as np
from scipy.sparse import lil_matrix
from scipy import linalg as la

[docs] def reducedForm(qq,qrows,qcols,bcols,neq,condn): """ Compute reduced-form coefficient matrix, b. Original author: Gary Anderson Original file downloaded from: http://www.federalreserve.gov/Pubs/oss/oss4/code.html This code is in the public domain and may be used freely. However the authors would appreciate acknowledgement of the source by citation of any of the following papers: Anderson, G. and Moore, G. "A Linear Algebraic Procedure for Solving Linear Perfect Foresight Models." Economics Letters, 17, 1985. Anderson, G. "Solving Linear Rational Expectations Models: A Horse Race" Computational Economics, 2008, vol. 31, issue 2, pages 95-113 Anderson, G. "A Reliable and Computationally Efficient Algorithm for Imposing the Saddle Point Property in Dynamic Models" Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3, pages 472-489 """ qs = lil_matrix(qq) left = list(range(0,qcols-qrows)) right = list(range(qcols-qrows,qcols)) nonsing = 1/np.linalg.cond(lil_matrix(qs[:,right]).todense()) > condn if nonsing: lu, piv = la.lu_factor(qs[:,right].toarray()) qs[:,left] = -la.lu_solve((lu,piv),qs[:,left].toarray(),trans=0) b = qs[0:neq,0:bcols] b = lil_matrix(b).todense() else: # rescale by dividing row by maximal qr element # inverse condition number small, rescaling themax = abs(qs[:,right]).max(axis=1) oneoverVector = list() for i in range(0,len(themax)): temp = 1 / themax[i] oneoverVector.append(temp) oneover = np.diag(oneoverVector) productMatrixRight = oneover * qs[:,right] nonsing = 1/np.linalg.cond(lil_matrix(productMatrixRight).todense()) > condn if nonsing: productMatrixLeft = oneover * qs[:,left] qs[:,left] = -productMatrixRight.I * productMatrixLeft b = qs[0:neq,0:bcols] b = lil_matrix(b).todense() return nonsing, b