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Robust Estimation and Inference for Importance Sampling Estimators with Infinite Variance

In: Essays in Honor of Cheng Hsiao

Author

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  • Joshua C. C. Chan
  • Chenghan Hou
  • Thomas Tao Yang

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.

Suggested Citation

  • Joshua C. C. Chan & Chenghan Hou & Thomas Tao Yang, 2020. "Robust Estimation and Inference for Importance Sampling Estimators with Infinite Variance," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 255-285, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320200000041008
    DOI: 10.1108/S0731-905320200000041008
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    More about this item

    Keywords

    Simulated maximum likelihood; bias correction; stochastic volatility; importance sampling; asymptotic trimming; Bayesian estimation; C11; C32; C52;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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