Consistency Properties of a Simulation-Base Estimator for Dynamic Processes
AbstractThis 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.
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Bibliographic InfoPaper provided by University of Miami, Department of Economics in its series Working Papers with number 0613.
Length: 22 pages
Date of creation: 25 Aug 2007
Date of revision:
Publication status: Forthcoming: In revision
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Markov process; simulation-based estimation; invariant probability; sample distribution; monotonicity; strong consistency;
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