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Controlling the size of autocorrelation robust tests

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  • Pötscher, Benedikt M.
  • Preinerstorfer, David

Abstract

Autocorrelation robust tests are notorious for suffering from size distortions and power problems. We investigate under which conditions the size of autocorrelation robust tests can be controlled by an appropriate choice of critical value.

Suggested Citation

  • Pötscher, Benedikt M. & Preinerstorfer, David, 2018. "Controlling the size of autocorrelation robust tests," Journal of Econometrics, Elsevier, vol. 207(2), pages 406-431.
  • Handle: RePEc:eee:econom:v:207:y:2018:i:2:p:406-431
    DOI: 10.1016/j.jeconom.2018.08.005
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    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2017. "Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing," MPRA Paper 81053, University Library of Munich, Germany.
    2. Sun, Yixiao & Yang, Jingjing, 2020. "Testing-optimal kernel choice in HAR inference," Journal of Econometrics, Elsevier, vol. 219(1), pages 123-136.
    3. Pötscher, Benedikt M. & Preinerstorfer, David, 2020. "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?," MPRA Paper 100234, University Library of Munich, Germany.
    4. Martínez-Iriarte, Julián & Sun, Yixiao & Wang, Xuexin, 2020. "Asymptotic F tests under possibly weak identification," Journal of Econometrics, Elsevier, vol. 218(1), pages 140-177.

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    More about this item

    Keywords

    Autocorrelation robust testing; Size control;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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