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Testing for seasonal unit roots in heterogeneous panels using monthly data in the presence of cross sectional dependence

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Listed:
  • Otero, Jesus
  • Smith, Jeremy
  • Giulietti, Monica

Abstract

This paper generalises the monthly seasonal unit root tests of Franses (1991) for a heterogeneous panel following the work of Im, Pesaran, and Shin (2003), which we refer to as the F-IPS tests. The paper presents the mean and variance necessary to yield a standard normal distribution for the tests, for di§erent number of time observations, T, and lag lengths. However, these tests are only applicable in the absence of cross-sectional dependence. Two alternative methods for modifying these F-IPS tests in the presence of cross-sectional dependency are presented: the Örst is the cross-sectionally augmented test, denoted CF-IPS, following Pesaran (2007), the other is a bootstap method, denoted BF-IPS. In general, the BF-IPS tests have greater power than the CF-IPS tests, although for large T and high degree of cross-sectional dependency the CF-IPS test dominates the BF-IPS test.

Suggested Citation

  • Otero, Jesus & Smith, Jeremy & Giulietti, Monica, 2008. "Testing for seasonal unit roots in heterogeneous panels using monthly data in the presence of cross sectional dependence," Economic Research Papers 269863, University of Warwick - Department of Economics.
  • Handle: RePEc:ags:uwarer:269863
    DOI: 10.22004/ag.econ.269863
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    References listed on IDEAS

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    1. Franses, Philip Hans, 1991. "Seasonality, non-stationarity and the forecasting of monthly time series," International Journal of Forecasting, Elsevier, vol. 7(2), pages 199-208, August.
    2. Otero, Jesus & Smith, Jeremy & Giulietti, Monica, 2007. "Testing for seasonal unit roots in heterogeneous panels in the presence of cross section dependence," Economics Letters, Elsevier, vol. 97(2), pages 179-184, November.
    3. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    4. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
    5. Otero, Jesus & Smith, Jeremy & Giulietti, Monica, 2005. "Testing for seasonal unit roots in heterogeneous panels," Economics Letters, Elsevier, vol. 86(2), pages 229-235, February.
    6. Chang, Yoosoon, 2004. "Bootstrap unit root tests in panels with cross-sectional dependency," Journal of Econometrics, Elsevier, vol. 120(2), pages 263-293, June.
    7. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
    8. Christian Dreger* & Hans-Eggert Reimers, 2005. "Panel Seasonal Unit Root Test: Further Simulation Results and An Application to Unemployment Data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 89(3), pages 321-337, August.
    9. Ghysels,Eric & Osborn,Denise R., 2001. "The Econometric Analysis of Seasonal Time Series," Cambridge Books, Cambridge University Press, number 9780521565882, January.
    10. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    11. Maddala, G S & Wu, Shaowen, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    12. Philip Hans Franses & Bart Hobijn, 1997. "Critical values for unit root tests in seasonal time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(1), pages 25-48.
    13. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
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    Cited by:

    1. Hsu Shih-Hsun, 2021. "Disentangling the source of non-stationarity in a panel of seasonal data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-18, February.

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    More about this item

    Keywords

    Agricultural and Food Policy; Research Methods/ Statistical Methods;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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