Consistency Properties of a Simulation-Based 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 0705.
Length: 22 pages
Date of creation: 25 Aug 2007
Date of revision:
Publication status: Forthcoming: Under Review
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More information through EDIRC
Markov process; simulation-based estimation; invariant probability; sample distribution; monotonicity; strong consistency.;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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