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February 2010 Consistency properties of a simulation-based estimator for dynamic processes
Manuel S. Santos
Ann. Appl. Probab. 20(1): 196-213 (February 2010). DOI: 10.1214/09-AAP608

Abstract

This paper considers a simulation-based estimator for a general class of Markovian processes and explores some strong consistency properties of the estimator. 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. The estimator is applied to an asset pricing model with technology adoption. A challenge for this model is to generate the observed high volatility of stock markets along with the much lower volatility of other real economic aggregates.

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Manuel S. Santos. "Consistency properties of a simulation-based estimator for dynamic processes." Ann. Appl. Probab. 20 (1) 196 - 213, February 2010. https://doi.org/10.1214/09-AAP608

Information

Published: February 2010
First available in Project Euclid: 8 January 2010

zbMATH: 1182.62168
MathSciNet: MR2582646
Digital Object Identifier: 10.1214/09-AAP608

Subjects:
Primary: 60K35 , 62M05
Secondary: 60B10 , 65C20

Keywords: invariant probability , Markov process , Monotonicity , sample distribution , simulation-based estimation , strong consistency

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.20 • No. 1 • February 2010
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