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Simulated Non-Parametric Estimation of Dynamic Models

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  • Filippo Altissimo
  • Antonio Mele

Abstract

This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel, which considerably simplifies bandwidth selection for the purpose of implementing the estimator. Furthermore, the SNE displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods introduced in this paper are fairly simple to implement, and possess finite sample properties that are well approximated by the asymptotic theory. We illustrate these features within typical estimation problems that arise in financial economics. Copyright , Wiley-Blackwell.

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File URL: http://hdl.handle.net/10.1111/j.1467-937X.2008.00527.x
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Bibliographic Info

Article provided by Oxford University Press in its journal The Review of Economic Studies.

Volume (Year): 76 (2009)
Issue (Month): 2 ()
Pages: 413-450

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Handle: RePEc:oup:restud:v:76:y:2009:i:2:p:413-450

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Cited by:
  1. Nickl, Richard & Pötscher, Benedikt M., 2009. "Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference," MPRA Paper 16608, University Library of Munich, Germany.
  2. Dennis Kristensen & Bernard Salanié, 2010. "Higher Order Improvements for Approximate Estimators," CAM Working Papers 2010-04, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
  3. Dennis Kristensen & Yongseok Shin, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-58, School of Economics and Management, University of Aarhus.
  4. Corradi, Valentina & Distaso, Walter & Mele, Antonio, 2013. "Macroeconomic determinants of stock volatility and volatility premiums," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 203-220.
  5. Valentina Corradi & Norman R. Swanson, 2011. "Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models," Post-Print peer-00796745, HAL.
  6. Aït-Sahalia, Yacine & Fan, Jianqing & Peng, Heng, 2009. "Nonparametric Transition-Based Tests for Jump Diffusions," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1102-1116.
  7. Carrasco, Marine & Chernov, Mikhail & Florens, Jean-Pierre & Ghysels, Eric, 2007. "Efficient estimation of general dynamic models with a continuum of moment conditions," Journal of Econometrics, Elsevier, vol. 140(2), pages 529-573, October.
  8. Giet, Ludovic & Lubrano, Michel, 2008. "A minimum Hellinger distance estimator for stochastic differential equations: An application to statistical inference for continuous time interest rate models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2945-2965, February.
  9. Takada, Teruko, 2009. "Simulated minimum Hellinger distance estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2390-2403, April.
  10. Gach, Florian & Pötscher, Benedikt M., 2010. "Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators," MPRA Paper 27512, University Library of Munich, Germany.
  11. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
  12. Dennis Kristensen & Bernard Salanie, 2013. "Higher-order properties of approximate estimators," CeMMAP working papers CWP45/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  13. Dunbar, Geoffrey, 2013. "Returns-to-scale and the equity premium puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1736-1754.

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