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Bandwidth Selection, Prewhitening, and the Power of the Phillips-Perron Test

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  • Cheung, Yin-Wong
  • Lai, Kon S.

Abstract

This study examines several important practical issues concerning nonparametric estimation of the innovation variance for the Phillips-Perron (PP) test. A Monte Carlo study is conducted to evaluate the potential effects of kernel choice, databased bandwidth selection, and prewhitening on the power property of the PP test in finite samples. The Monte Carlo results are instructive. Although the kernel choice is found to make little difference, data-based bandwidth selection and prewhitening can lead to power gains for the PP test. The combined use of both the Andrews (1991, Ecpnometrica 59, 817–858) data-based bandwidth selection procedure and the Andrews and Monahan (1992, Econometrica 60, 953–966) prewhitening procedure performs particularly well. With the combined use of these two procedures, the PPtest displays relatively good power in comparison with the augmented Dickey-Fuller test.

Suggested Citation

  • Cheung, Yin-Wong & Lai, Kon S., 1997. "Bandwidth Selection, Prewhitening, and the Power of the Phillips-Perron Test," Econometric Theory, Cambridge University Press, vol. 13(5), pages 679-691, October.
    • Handle: RePEc:cup:etheor:v:13:y:1997:i:05:p:679-691_00
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    Cited by:

    1. Ingolf Dittmann, 2000. "Residual‐Based Tests For Fractional Cointegration: A Monte Carlo Study," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(6), pages 615-647, November.
    2. Dong Mu & Salman Hanif & Khalid Mehmood Alam & Omer Hanif, 2022. "A Correlative Study of Modern Logistics Industry in Developing Economy and Carbon Emission Using ARDL: A Case of Pakistan," Mathematics, MDPI, vol. 10(4), pages 1-18, February.
    3. Mesut Yilmaz & Yessengali Oskenbayev & Kanat Abdulla, 2009. "Currency Substitution: A Case Of Kazakhstan (2000:1-2007:12)," William Davidson Institute Working Papers Series wp946, William Davidson Institute at the University of Michigan.
    4. Lima Luiz Renato & Xiao Zhijie, 2010. "Testing Unit Root Based on Partially Adaptive Estimation," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-34, June.
    5. Ormos, Mihály & Erdős, Péter, 2011. "Borok mint alternatív befektetési lehetőségek [Wines as an alternative investment]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 158-172.
    6. Ingolf Dittmann, 2001. "Fractional cointegration of voting and non-voting shares," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 321-332.
    7. Horowitz, Joel L. & Savin, N. E., 2000. "Empirically relevant critical values for hypothesis tests: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 95(2), pages 375-389, April.
    8. Vilasuso, Jon, 2001. "Causality tests and conditional heteroskedasticity: : Monte Carlo evidence," Journal of Econometrics, Elsevier, vol. 101(1), pages 25-35, March.
    9. Zou, Nan & Politis, Dimitris N., 2019. "Linear process bootstrap unit root test," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 74-80.

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