Parallel algorithms for large scale econometric models

In this paper we have developed algorithms to solve macroeconometric models with forward-looking variables based on Newton method for nonlinear systems of equations. The most difficult step for Newton methods represents the resolution of a large linear system for each iteration. Thus, we compare the performances resulted by solving this linear system using two iterative methods and the direct method. We’ve also described an implementation of the parallel versions of such algorithms using a software package. Our experiments confirm that the iterative methods have a low computational complexity and storage requirements, but the parallel versions of direct methods show a superior speedup.