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Analyzing Unit Root Tests in Finite Samples Using Power Profiles

Author

Listed:
  • Kilian, L.
  • Caner, M.

Abstract

This study analyzes the size and power of tests of the null of stationarity against the unit root alternative. Existing evidence is limited to processes with roots between 0 and 0.7. In sharp contrast, virtually all applications of economic interest involve null hypotheses much closer to 1. We focus on the Leybourne and McCabe (1994) test because of its superior small-sample properties. We document that conventional asymptotic critical values for this test are inappropriate in small samples, and we provide finite-sample critical values for the economically relevant range of parameter values and sample sizes. Using power profiles, we show that it is very difficult to obtain meaningful results, even with large samples.

Suggested Citation

  • Kilian, L. & Caner, M., 1998. "Analyzing Unit Root Tests in Finite Samples Using Power Profiles," Papers 98-05, Michigan - Center for Research on Economic & Social Theory.
    • Handle: RePEc:fth:michet:98-05
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    Citations

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    Cited by:

    1. Meier Carsten-Patrick, 2001. "Trend und Zyklus im Bruttoinlandsprodukt der Bundesrepublik Deutschland. Eine Anmerkung / Trends and Cycles in Germany’s Real Gross Domestic Product. A Note," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 221(2), pages 168-178, April.
    2. Meier, Carsten-Patrick, 2000. "Trend und Zyklus im Bruttoinlandsprodukt der Bundesrepublik Deutschland - eine Anmerkung," Kiel Working Papers 993, Kiel Institute for the World Economy (IfW Kiel).

    More about this item

    Keywords

    UNIT ROOTS ; ECONOMETRICS ; SAMPLING;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - 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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