Robust Estimation and Inference for Importance Sampling Estimators with Infinite Variance*

Joshua Chan, Chenghan Hou and Thomas Tao Yang

A chapter in Essays in Honor of Cheng Hsiao, 2020, vol. 41, pp 255-285 from Emerald Group Publishing Limited

Abstract: Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors consider asymptotic trimming in such a setting. Specifically, the authors propose a bias-corrected tail-trimmed estimator such that it is consistent and has finite variance. The authors show that the proposed estimator is asymptotically normal, and has good finite-sample properties in a Monte Carlo study.

Keywords: Simulated maximum likelihood; bias correction; stochastic volatility; importance sampling; asymptotic trimming; Bayesian estimation; C11; C32; C52 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320200000041008

DOI: 10.1108/S0731-905320200000041008

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