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Consistency Properties of a Simulation-Based Estimator for Dynamic Processes

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  • Manuel Santos

    (Department of Economics, University of Miami)

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    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.

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    Bibliographic Info

    Paper provided by University of Miami, Department of Economics in its series Working Papers with number 0705.

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    Length: 22 pages
    Date of creation: 25 Aug 2007
    Date of revision:
    Publication status: Forthcoming: Under Review
    Handle: RePEc:mia:wpaper:0705

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    Related research

    Keywords: Markov process; simulation-based estimation; invariant probability; sample distribution; monotonicity; strong consistency.;

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    References

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    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Lee, Bong-Soo & Ingram, Beth Fisher, 1991. "Simulation estimation of time-series models," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 197-205, February.
    3. Duffie, Darrell & Singleton, Kenneth J, 1993. "Simulated Moments Estimation of Markov Models of Asset Prices," Econometrica, Econometric Society, vol. 61(4), pages 929-52, July.
    4. Hopenhayn, Hugo A & Prescott, Edward C, 1992. "Stochastic Monotonicity and Stationary Distributions for Dynamic Economies," Econometrica, Econometric Society, vol. 60(6), pages 1387-406, November.
    5. Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez & Manuel Santos, 2004. "Convergence properties of the likelihood of computed dynamic models," Working Paper 2004-27, Federal Reserve Bank of Atlanta.
    6. Mirman, Leonard J. & Morand, Olivier F. & Reffett, Kevin L., 2008. "A qualitative approach to Markovian equilibrium in infinite horizon economies with capital," Journal of Economic Theory, Elsevier, vol. 139(1), pages 75-98, March.
    7. Ariel Pakes, 1986. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," NBER Working Papers 1340, National Bureau of Economic Research, Inc.
    8. Futia, Carl A, 1982. "Invariant Distributions and the Limiting Behavior of Markovian Economic Models," Econometrica, Econometric Society, vol. 50(2), pages 377-408, March.
    9. Carrasco, Marine & Florens, Jean-Pierre, 2002. "Simulation-Based Method of Moments and Efficiency," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 482-92, October.
    10. Pakes, Ariel S, 1986. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," Econometrica, Econometric Society, vol. 54(4), pages 755-84, July.
    11. Santos, Manuel S., 2004. "Simulation-based estimation of dynamic models with continuous equilibrium solutions," Journal of Mathematical Economics, Elsevier, vol. 40(3-4), pages 465-491, June.
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