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Improving the Performance of a Long-Run Variance Ratio Test for a Unit Root

Author

Listed:
  • Hugo Ferrer-Pérez

    (Center for Agro-Food Economy and Development)

  • María-Isabel Ayuda

    (University of Zaragoza)

  • Antonio Aznar

    (University of Zaragoza)

Abstract

Cai and Shintani (2006, Econometric Theory, 22, 347–372) considered the impact of introducing an inconsistent long-run variance estimator when constructing a class of kernel-based ratio tests for testing non-stationarity in the series. They found that the quotient of two estimators with different rates of convergence under the null and the alternative hypotheses may lead to a test having an interesting size and power trade-off. This paper develops modified versions of this test, presents new asymptotic results and tabulates critical values. The finite sample performance is explored through Monte Carlo simulations. The results show that the modifications proposed lead to more powerful unit root tests.

Suggested Citation

  • Hugo Ferrer-Pérez & María-Isabel Ayuda & Antonio Aznar, 2019. "Improving the Performance of a Long-Run Variance Ratio Test for a Unit Root," The Japanese Economic Review, Springer, vol. 70(2), pages 258-274, June.
    • Handle: RePEc:spr:jecrev:v:70:y:2019:i:2:d:10.1111_jere.12185
    DOI: 10.1111/jere.12185
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    References listed on IDEAS

    as
    1. Cai, Ye & Shintani, Mototsugu, 2006. "On The Alternative Long-Run Variance Ratio Test For A Unit Root," Econometric Theory, Cambridge University Press, vol. 22(3), pages 347-372, June.
    2. Perron, Pierre & Qu, Zhongjun, 2007. "A simple modification to improve the finite sample properties of Ng and Perron's unit root tests," Economics Letters, Elsevier, vol. 94(1), pages 12-19, January.
    3. Nicholas M. Kiefer & Timothy J. Vogelsang, 2002. "Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation," Econometrica, Econometric Society, vol. 70(5), pages 2093-2095, September.
    4. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    5. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    6. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    7. Elliott, Graham, 1999. "Efficient Tests for a Unit Root When the Initial Observation Is Drawn from Its Unconditional Distribution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(3), pages 767-783, August.
    8. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    10. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2009. "Unit Root Testing In Practice: Dealing With Uncertainty Over The Trend And Initial Condition," Econometric Theory, Cambridge University Press, vol. 25(3), pages 587-636, June.
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    More about this item

    Keywords

    C12; C22;

    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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