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Simulated non-parametric estimation of dynamic models

Altissimo, Filippo and Mele, Antonio (2009) Simulated non-parametric estimation of dynamic models. Review of Economic Studies, 76 (2). pp. 413-450. ISSN 0034-6527

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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.

Item Type: Article
Official URL: http://restud.oxfordjournals.org/
Additional Information: © 2009 The Review of Economic Studies Ltd.
Library of Congress subject classification: H Social Sciences > HB Economic Theory
Sets: Departments > Finance
Collections > Economists Online
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 08 Feb 2011 11:59
URL: http://eprints.lse.ac.uk/32268/

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