TY - JOUR
T1 - Testing the assumptions behind importance sampling
AU - Koopman, S.J.
AU - Shephard, N.
AU - Creal, D.D.
PY - 2009
Y1 - 2009
N2 - 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. © 2008 Elsevier B.V. All rights reserved.
AB - 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. © 2008 Elsevier B.V. All rights reserved.
UR - https://www.scopus.com/pages/publications/63149119676
UR - https://www.scopus.com/inward/citedby.url?scp=63149119676&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2008.10.002
DO - 10.1016/j.jeconom.2008.10.002
M3 - Article
SN - 0304-4076
VL - 149
SP - 2
EP - 11
JO - Journal of Econometrics
JF - Journal of Econometrics
ER -