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

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  • Kenneth L. Judd
  • Lilia Maliar
  • Serguei Maliar

Abstract

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.

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Article provided by Econometric Society in its journal Quantitative Economics.

Volume (Year): 2 (2011)
Issue (Month): 2 (07)
Pages: 173-210

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Handle: RePEc:ecm:quante:v:2:y:2011:i:2:p:173-210

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  1. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
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