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Stationary bootstrapping for cointegrating regressions

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  • Shin, Dong Wan
  • Hwang, Eunju

Abstract

The validity of stationary bootstrapping is investigated for cointegrating regressions in large samples as well as in finite samples. The bootstrap ordinary least squares estimator (OLSE) is shown to be valid in large samples having the same limiting distribution as the OLSE under a similar normalization. Large sample validity of a bootstrap test regarding cointegration parameters is also established. Finite sample size and power properties of the bootstrap test are investigated via a Monte Carlo experiment.

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  • Shin, Dong Wan & Hwang, Eunju, 2013. "Stationary bootstrapping for cointegrating regressions," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 474-480.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:2:p:474-480
    DOI: 10.1016/j.spl.2012.10.007
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    References listed on IDEAS

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    4. Eunju Hwang & Dong Wan Shin, 2011. "Stationary bootstrapping for non‐parametric estimator of nonlinear autoregressive model," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 292-303, May.
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    Cited by:

    1. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.
    2. Hwang, Eunju & Shin, Dong Wan, 2015. "Stationary bootstrapping for semiparametric panel unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 14-25.

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