SPARSE DIRECT-METHODS FOR MODEL SIMULATION

In this paper, different strategies to exploit the sparse structure in the solution techniques for macroeconometric models with forward-look ing variables are discussed. First, the stacked model is decomposed in to recursive submodels without destroying its original block pattern. Next, we concentrate on how to efficiently solve the sparse linear sys tem in the Newton algorithm. In this frame, a multiple block diagonal LU factorization and a sparse Gaussian elimination are presented. The algorithms are compared by solving the country model for Japan in MULT IMOD.