Journal Article

Simulated Non-Parametric Estimation of Dynamic Models

Filippo Altissimo and Antonio Mele

in The Review of Economic Studies

Published on behalf of Review of Economic Studies Ltd

Volume 76, issue 2, pages 413-450
Published in print April 2009 | ISSN: 0034-6527
Published online April 2009 | e-ISSN: 1467-937X | DOI: https://dx.doi.org/10.1111/j.1467-937X.2008.00527.x
Simulated Non-Parametric Estimation of Dynamic Models

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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.

Keywords: C13; C14

Journal Article.  15843 words.  Illustrated.

Subjects: Econometric and Statistical Methods and Methodology: General

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