Consistency Properties of a Simulation-Base Estimator for Dynamic Processes

Manuel Santos

No 613, Working Papers from University of Miami, Department of Economics

Abstract: This paper considers a simulation-based estimator for a general class of Markovian processes and explores some strong consistency properties of the estimator. These results are of interest for various kinds of simulation-based estimation methods typically used in economics and finance. The estimation problem is defined over a continuum of invariant distributions indexed by a vector of parameters. A key step in the method of proof is to show the uniform convergence (a.s.) of a family of sample distributions over the domain of parameters. This uniform convergence holds under mild continuity and monotonicity conditions on the dynamic process.

Keywords: Markov process; simulation-based estimation; invariant probability; sample distribution; monotonicity; strong consistency (search for similar items in EconPapers)
JEL-codes: C3 J12 J13 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2007-08-25
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Forthcoming: In revision

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Related works:
Working Paper: Consistency properties of a simulation-based estimator for dynamic processes (2010)
Working Paper: Consistency Properties of a Simulation-Based Estimator for Dynamic Processes (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:mia:wpaper:0613

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