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

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  • Hugo Ferrer‐Pérez
  • María‐Isabel Ayuda
  • Antonio Aznar

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, Japanese Economic Association, vol. 70(2), pages 258-274, June.
    • Handle: RePEc:bla:jecrev:v:70:y:2019:i:2:p:258-274
    DOI: 10.1111/jere.12185
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    References listed on IDEAS

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    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.
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    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.
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    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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