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Simulated Non-Parametric Estimation of Dynamic Models

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Author Info
FILIPPO ALTISSIMO
ANTONIO MELE

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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 © 2009 The Review of Economic Studies Limited.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-937X.2008.00527.x
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Publisher Info
Article provided by Blackwell Publishing in its journal Review of Economic Studies.

Volume (Year): 76 (2009)
Issue (Month): 2 (04)
Pages: 413-450
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Handle: RePEc:bla:restud:v:76:y:2009:i:2:p:413-450

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  1. Nickl, Richard & Pötscher, Benedikt M., 2009. "Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference," MPRA Paper 16608, University Library of Munich, Germany. [Downloadable!]
  2. 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. [Downloadable!]
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This page was last updated on 2009-11-22.

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