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Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models

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  • Sílvia Gonçalves
  • Ulrich Hounyo
  • Andrew J. Patton
  • Kevin Sheppard

Abstract

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors.

Suggested Citation

  • Sílvia Gonçalves & Ulrich Hounyo & Andrew J. Patton & Kevin Sheppard, 2023. "Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 683-694, July.
    • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:3:p:683-694
    DOI: 10.1080/07350015.2022.2058949
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    Cited by:

    1. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org.
    2. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.

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