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Efficient bootstrap with weakly dependent processes

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  • Bravo, Francesco
  • Crudu, Federico

Abstract

The efficient bootstrap methodology is developed for overidentified moment conditions models with weakly dependent observation. The resulting bootstrap procedure is shown to be asymptotically valid and can be used to approximate the distributions of t-statistics, the J-statistic for overidentifying restrictions, and Wald, Lagrange multiplier and distance statistics for nonlinear hypotheses. The asymptotic validity of the efficient bootstrap based on a computationally less demanding approximate k-step estimator is also shown. The finite sample performance of the proposed bootstrap is assessed using simulations in an intertemporal consumption based asset pricing model.

Suggested Citation

  • Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3444-3458
    DOI: 10.1016/j.csda.2010.07.021
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    1. 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.

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    More about this item

    Keywords

    α-mixing; Consumption CAPM; GEL; GMM; Hypothesis testing;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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