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Generalised Empirical Likelihood Kernel Block Bootstrapping

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  • Paulo M.D.C. Parente
  • Richard J. Smith

Abstract

This article unveils how the kernel block bootstrap method of Parente and Smith (2018a,2018b) can be applied to make inferences on parameters of models de ned through moment restrictions. Bootstrap procedures that resort to generalised empirical likelihood implied probabilities to draw observations are also introduced. We prove the rst-order asymptotic validity of bootstrapped test statistics for overidentifying moment restrictions, parametric restrictions and additional moment restrictions. Resampling methods based on such probabilities were shown to be efficient by Brown and Newey (2002). A set of simulation experiments reveals that the statistical tests based on the proposed bootstrap methods perform better than those that rely on first-order asymptotic theory.

Suggested Citation

  • Paulo M.D.C. Parente & Richard J. Smith, 2018. "Generalised Empirical Likelihood Kernel Block Bootstrapping," Working Papers REM 2018/55, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp0552018
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    References listed on IDEAS

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    Cited by:

    1. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    2. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.

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    More about this item

    Keywords

    Bootstrap; heteroskedastic and autocorrelation consistent inference; Generalised Method of Moments; Generalised Empirical Likelihood;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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

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