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Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an Application to the Prebisch-Singer Hypothesis

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  • Bunzel, Helle
  • Vogelsang, Timothy J.

Abstract

In this paper we propose tests for hypothesis regarding the parameters of a the deterministic trend function of a univariate time series. The tests do not require knowledge of the form of serial correlation in the data and they are robust to strong serial correlation. The data can contain a unit root and the tests still have the correct size asymptotically. The tests we analyze are standard heteroskedasticity autocorrelation (HAC) robust tests based on nonparametric kernel variance estimators. We analyze these tests using the small-b asymptotic framework recently proposed by Kiefer and Vogelsang (2002). This analysis allows us to analyze the power properties of the tests with regards to bandwidth and kernel choices. Our analysis shows that among popular kernels, there are specific kernel and bandwidth choices that deliver tests with maximal power within a specific class of tests. We apply the recommended tests to the logarithm of a net barter terms of trade series and we find that this series has a statistically significant negative slope. This finding is consistent with the well known Prebisch-Singer hypothesis. Because our tests are robust to strong serial correlation or a unit root in the data, our results in support of the Prebisch-Singer hypothesis are relatively strong.
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Suggested Citation

  • Bunzel, Helle & Vogelsang, Timothy J., 2005. "Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an Application to the Prebisch-Singer Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 381-394, October.
  • Handle: RePEc:bes:jnlbes:v:23:y:2005:p:381-394
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    References listed on IDEAS

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    1. Sapsford, D, 1985. "The Statistical Debate on the Net Barter Terms of Trade between Primary Commodities and Manufactures: A Comment and Some Additional Evidence," Economic Journal, Royal Economic Society, vol. 95(379), pages 781-788, September.
    2. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(06), pages 1130-1164, December.
    3. Ardeni, Pier Giorgio & Wright, Brian, 1992. "The Prebisch-Singer Hypothesis: A Reappraisal Independent of Stationarity Hypotheses," Economic Journal, Royal Economic Society, vol. 102(413), pages 803-812, July.
    4. Spraos, John, 1980. "The Statistical Debate on the Net Barter Terms of Trade between Primary Commodities and Manufactures," Economic Journal, Royal Economic Society, vol. 90(357), pages 107-128, March.
    5. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    6. Peter C.B. Phillips & Victor Solo, 1989. "Asymptotics for Linear Processes," Cowles Foundation Discussion Papers 932, Cowles Foundation for Research in Economics, Yale University.
    7. Lutz, Matthias G, 1999. "A General Test of the Prebisch-Singer Hypothesis," Review of Development Economics, Wiley Blackwell, vol. 3(1), pages 44-57, February.
    8. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    9. Powell, Andrew, 1991. "Commodity and Developing Country Terms of Trade: What Does the Long Run Show?," Economic Journal, Royal Economic Society, vol. 101(409), pages 1485-1496, November.
    10. Cuddington, John T & Urzua, Carlos M, 1989. "Trends and Cycles in the Net Barter Terms of Trade: A New Approach," Economic Journal, Royal Economic Society, vol. 99(396), pages 426-442, June.
    11. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
    12. Breitung, Jorg, 2002. "Nonparametric tests for unit roots and cointegration," Journal of Econometrics, Elsevier, vol. 108(2), pages 343-363, June.
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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