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Analysis of numerical errors

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  • Adrian Peralta-Alva
  • Manuel S. Santos

Abstract

This paper provides a general framework for the quantitative analysis of stochastic dynamic models. We review convergence properties of some numerical algorithms and available methods to bound approximation errors. We then address convergence and accuracy properties of the simulated moments. Our purpose is to provide an asymptotic theory for the computation, simulation-based estimation, and testing of dynamic economies. The theoretical analysis is complemented with several illustrative examples. We study both optimal and non-optimal economies. Optimal economies generate smooth laws of motion defining Markov equilibria, and can be approximated by recursive methods with contractive properties. Non-optimal economies, however, lack existence of continuous Markov equilibria, and need to be computed by other algorithms with weaker approximation properties.

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2012-062.

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Date of creation: 2012
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Handle: RePEc:fip:fedlwp:2012-062

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Keywords: Error analysis (Mathematics) ; Markov processes;

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Cited by:
  1. Martin M. Andreasen & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2013. "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications," CREATES Research Papers 2013-12, School of Economics and Management, University of Aarhus.

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