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Trend-stationary GNP: evidence from a new exact pointwise most powerful invariant unit root test

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  • Philip A. Shively

    (Department of Economics and Business, Lafayette College, Easton, PA 18042 USA)

Abstract

There has been a substantial debate whether GNP has a unit root. However, statistical tests have had little success in distinguishing between unit-root and trend-reverting specifications because of poor statistical properties. This paper develops a new exact small-sample, pointwise most powerful unit root test that is invariant to the unknown mean and scale of the time series tested, that generates exact small-sample critical values, powers and p-values, that has power which approximates the maximum possible power, and that is highly robust to conditional heteroscedasticity. This test decisively rejects the unit root null hypothesis when applied to annual US real GNP and US real per capita GNP series. This paper also develops a modified version of the test to address whether a time series contains a permanent, unit root process in addition to a temporary, stationary process. It shows that if these GNP series contain a unit root process in addition to the stationary process, then it is most likely very small. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Philip A. Shively, 2001. "Trend-stationary GNP: evidence from a new exact pointwise most powerful invariant unit root test," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(4), pages 537-551.
  • Handle: RePEc:jae:japmet:v:16:y:2001:i:4:p:537-551
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    File URL: http://qed.econ.queensu.ca:80/jae/2001-v16.4/
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    Cited by:

    1. Broda, Simon & Carstensen, Kai & Paolella, Marc S., 2007. "Bias-adjusted estimation in the ARX(1) model," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3355-3367, April.
    2. Brzoza-Brzezina, Michał & Chmielewski, Tomasz & Niedźwiedzińska, Joanna, 2007. "Substitution between domestic and foreign currency loans in Central Europe. Do central banks matter?," MPRA Paper 6759, University Library of Munich, Germany.
    3. Berg, Nathan, 2004. "No-decision classification: an alternative to testing for statistical significance," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 631-650, November.
    4. Maxwell L. King & Sivagowry Sriananthakumar, 2015. "Point Optimal Testing: A Survey of the Post 1987 Literature," Monash Econometrics and Business Statistics Working Papers 5/15, Monash University, Department of Econometrics and Business Statistics.

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