Efficient High-Dimensional Importance Sampling
AbstractThe paper describes a simple, generic and yet highly accurate Efficient Importance Sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions. It can be used to evaluate likelihood functions and byproducts thereof, such as ML estimators, for models which depend upon unobservable variables. A dynamic stochastic volatility model and a logit panel data model with unobserved heterogeneity (random effects) in both dimensions are used to provide illustrations of EIS high numerical accuracy, even under small number of MC draws. MC simulations are used to characterize the finite sample numerical and statistical properties of EIS-based ML estimators.
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Bibliographic InfoPaper provided by University of Pittsburgh, Department of Economics in its series Working Papers with number 321.
Date of creation: Jun 2007
Date of revision: Jan 2007
Publication status: Forthcoming in Journal of Econometrics
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