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

• Kenneth Judd ^[1] ; Lilia Maliar ^[2] ; Serguei Maliar ^[2]
  1. [1] Stanford University

     Stanford University

     Estados Unidos

     
  2. [2] Universitat d'Alacant

     Universitat d'Alacant

     Alicante, España

     
• Localización: Working papers = Documentos de trabajo: Serie AD, Nº. 15, 2011, págs. 1-60
• Idioma: inglés
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• Resumen

  • 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.