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Analysis of numerical errors

Listed author(s):
  • Adrian Peralta-Alva
  • Manuel S. Santos

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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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
Handle: RePEc:fip:fedlwp:2012-062
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  7. Greenwood, Jeremy & Huffman, Gregory W., 1993. "On the existence of nonoptimal equilibria in dynamic stochastic economies," Working Papers 9330, Federal Reserve Bank of Dallas.
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  9. Kehoe, Timothy J & Levine, David K, 2001. "Liquidity Constrained Markets versus Debt Constrained Markets," Econometrica, Econometric Society, vol. 69(3), pages 575-598, May.
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  11. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez & Manuel Santos, 2004. "Convergence Properties of the Likelihood of Computed Dynamic Models," PIER Working Paper Archive 04-034, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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  17. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
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  19. Hellwig, Martin F., 1983. "A note on the implementation of rational expectations equilibria," Economics Letters, Elsevier, vol. 11(1-2), pages 1-8.
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