Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models

By Kenneth L. Judd, Lilia Maliar, Serguei Maliar. 2011

132 citations recorded by the source corpus.

We develop numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. First, instead of standard least-squares methods, we examine a variety of alternatives, including least-squares methods using singular value decomposition and Tikhonov regularization, least-absolute deviations methods, and principal component regression method, all of which are numerically stable and can handle ill-conditioned problems. Second, instead of conventional Monte Carlo integration, we use accurate quadrature and monomial integration. We test our generalized stochastic simulation algorithm (GSSA) in three applications: the standard representative agent neoclassical growth model, a model with rare disasters and a multi-country models with hundreds of state variables. GSSA is simple to program, and MATLAB codes are provided.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/dc5a0af89a26a0153b5028d6e03811070cce7292

Citations in the corpus