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Testing the assumptions behind importance sampling

[Zeitschriftenartikel]

Koopman, Siem Jan; Shephard, Neil; Creal, Drew

Zitationshinweis

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Abstract Importance sampling is used in many areas of modern econometrics to approximate unsolvable integrals. Its reliable use requires the sampler to possess a variance, for this guarantees a square root speed of convergence and asymptotic normality of the estimator of the integral. However, this assumption is seldom checked. In this paper we use extreme value theory to empirically assess the appropriateness of this assumption. Our main application is the stochastic volatility model, where importance sampling is commonly used for maximum likelihood estimation of the parameters of the model.
Thesaurusschlagwörter simulation
Klassifikation Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Freie Schlagwörter Extreme value theory; Importance sampling; Stochastic volatility
Sprache Dokument Englisch
Publikationsjahr 2009
Seitenangabe S. 2-11
Zeitschriftentitel Journal of Econometrics, 149 (2009) 1
DOI http://dx.doi.org/10.1016/j.jeconom.2008.10.002
Status Postprint; begutachtet (peer reviewed)
Lizenz PEER Licence Agreement (applicable only to documents from PEER project)
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