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

  1. Kenneth Judd 1
  2. Lilia Maliar 2
  3. Serguei Maliar 2
  1. 1 Stanford University
    info

    Stanford University

    Stanford, Estados Unidos

    ROR https://ror.org/00f54p054

  2. 2 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Revista:
Working papers = Documentos de trabajo: Serie AD

Año de publicación: 2011

Número: 15

Páginas: 1-60

Tipo: Documento de Trabajo

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.