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Testing the assumptions behind importance sampling

[journal article]

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

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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.
Keywords simulation
Classification Economic Statistics, Econometrics, Business Informatics
Free Keywords Extreme value theory; Importance sampling; Stochastic volatility
Document language English
Publication Year 2009
Page/Pages p. 2-11
Journal Journal of Econometrics, 149 (2009) 1
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)