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Why Frequency Matters for Unit Root Testing

Author

Listed:
  • H. Peter Boswijk

    () (Faculty of Economics and Econometrics, Universiteit van Amsterdam)

  • Franc Klaassen

    () (Faculty of Economics and Econometrics, Universiteit van Amsterdam)

Abstract

It is generally believed that for the power of unit root tests, only the time span and not the observation frequency matters. In this paper we show that the observation frequency does matter when the high-frequency data display fat tails and volatility clustering, as is typically the case for financial time series such as exchange rate returns. Our claim builds on recent work on unit root and cointegration testing based non-Gaussian likelihood functions. The essential idea is that such methods will yield power gains in the presence of fat tails and persistent volatility clustering, and the strength of these features (and hence the power gains) increases with the observation frequency. This is illustrated using both Monte Carlo simulations and empirical applications to real exchange rates. See also the article in Journal of Business & Economic Statistics .

Suggested Citation

  • H. Peter Boswijk & Franc Klaassen, 2005. "Why Frequency Matters for Unit Root Testing," Tinbergen Institute Discussion Papers 04-119/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20040119
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    File URL: http://papers.tinbergen.nl/04119.pdf
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    References listed on IDEAS

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    1. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Chapters,in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220 National Bureau of Economic Research, Inc.
    2. Shiller, Robert J. & Perron, Pierre, 1985. "Testing the random walk hypothesis : Power versus frequency of observation," Economics Letters, Elsevier, vol. 18(4), pages 381-386.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "A panel project on purchasing power parity: Mean reversion within and between countries," Journal of International Economics, Elsevier, vol. 40(1-2), pages 209-224, February.
    5. Boswijk, H. Peter & Lucas, Andre, 2002. "Semi-nonparametric cointegration testing," Journal of Econometrics, Elsevier, vol. 108(2), pages 253-280, June.
    6. Alan M. Taylor, 2002. "A Century Of Purchasing-Power Parity," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 139-150, February.
    7. Lothian, James R & Taylor, Mark P, 1996. "Real Exchange Rate Behavior: The Recent Float from the Perspective of the Past Two Centuries," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 488-509, June.
    8. Ling, Shiqing & Li, W.K., 2003. "Asymptotic Inference For Unit Root Processes With Garch(1,1) Errors," Econometric Theory, Cambridge University Press, vol. 19(04), pages 541-564, August.
    9. Seo, Byeongseon, 1999. "Distribution theory for unit root tests with conditional heteroskedasticity1," Journal of Econometrics, Elsevier, vol. 91(1), pages 113-144, July.
    10. Lucas, André, 1997. "Cointegration Testing Using Pseudolikelihood Ratio Tests," Econometric Theory, Cambridge University Press, vol. 13(02), pages 149-169, April.
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    Citations

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    Cited by:

    1. Ghoshray, Atanu & Johnson, Ben, 2010. "Trends in world energy prices," Energy Economics, Elsevier, vol. 32(5), pages 1147-1156, September.
    2. Maslyuk, Svetlana & Smyth, Russell, 2008. "Unit root properties of crude oil spot and futures prices," Energy Policy, Elsevier, vol. 36(7), pages 2591-2600, July.
    3. Ozdemir, Zeynel Abidin & Gokmenoglu, Korhan & Ekinci, Cagdas, 2013. "Persistence in crude oil spot and futures prices," Energy, Elsevier, vol. 59(C), pages 29-37.

    More about this item

    Keywords

    Fat tails; GARCH; mean reversion; observation frequency; purchasing-power parity; unit roots;

    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
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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