Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models.

We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economic models. It consists in constructing a lower bound on the size of approximation errors. A small lower bound on errors is a necessary condition for accuracy: If a lower error bound is unacceptably lar...

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Publicado en:Econometrica Vol. 85; no. 3; pp. 991 - 1013
Autores principales: Judd, Kenneth L., Maliar, Lilia, Maliar, Serguei
Formato: Artículo
Publicado: Wiley-Blackwell May2017
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May2017
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      pub: Wiley-Blackwell
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        atl: Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models.
      aug:
        au:
          Judd, Kenneth L.
          Maliar, Lilia
          Maliar, Serguei
        affil:
          Hoover Institution, Stanford University
          NBER
          Dept. of Economics, Stanford University
          Dept. of Economics, University of Alicante
          Dept. of Economics, Lucas Hall, Leavey School of Business, Santa Clara University
      su:
        Economic models
        Errors
        Equations
        Approximation theory
        Chebyshev systems
      sug:
        subj:
          Economic models
          Errors
          Equations
          Approximation theory
          Chebyshev systems
      keyword:
        accuracy
        approximate solution
        Approximation errors
        backward error analysis
        error bound
        Euler equation residuals
        forward error analysis
        lower error bound
        new Keynesian model
        numerical solution
        upper error bound
        accuracy
        approximate solution
        Approximation errors
        backward error analysis
        error bound
        Euler equation residuals
        forward error analysis
        lower error bound
        new Keynesian model
        numerical solution
        upper error bound
      ab: We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economic models. It consists in constructing a lower bound on the size of approximation errors. A small lower bound on errors is a necessary condition for accuracy: If a lower error bound is unacceptably large, then the actual approximation errors are even larger, and hence, the approximation is inaccurate. Our lower-bound error analysis is complementary to the conventional upper-error (worst-case) bound analysis, which provides a sufficient condition for accuracy. As an illustration of our methodology, we assess approximation in the first- and second-order perturbation solutions for two stylized models: a neoclassical growth model and a new Keynesian model. The errors are small for the former model but unacceptably large for the latter model under some empirically relevant parameterizations.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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