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More powerful cointegration tests with non-normal errors

Author

Listed:
  • Lee Hyejin
  • Lee Junsoo

    (Department of Economics, Finance, and Legal Studies, University of Alabama, Tuscaloosa, AL 35487, USA)

  • Im Kyungso

    (Federal deposit Insurance Corporation (FDIC), 550 17th Street, NW, Washington, DC, USA)

Abstract

In this paper, we suggest new cointegration tests that can become more powerful in the presence of non-normal errors. Non-normal errors will not pose a problem in usual cointegration tests even when they are ignored. However, we show that they can become useful sources to improve the power of the tests when we use the “residual augmented least squares” (RALS) procedure to make use of nonlinear moment conditions driven by non-normal errors. The suggested testing procedure is easy to implement and it does not require any non-linear estimation techniques. We can exploit the information on the non-normal error distribution that is already available but ignored in the usual cointegration tests. Our simulation results show significant power gains over existing cointegration tests in the presence of non-normal errors.

Suggested Citation

  • Lee Hyejin & Lee Junsoo & Im Kyungso, 2015. "More powerful cointegration tests with non-normal errors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 397-413, September.
  • Handle: RePEc:bpj:sndecm:v:19:y:2015:i:4:p:397-413:n:1
    DOI: 10.1515/snde-2013-0060
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    References listed on IDEAS

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    1. Im, Kyung So & Schmidt, Peter, 2008. "More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares," Journal of Econometrics, Elsevier, vol. 144(1), pages 219-233, May.
    2. Peter Boswijk, H., 1994. "Testing for an unstable root in conditional and structural error correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 37-60, July.
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    4. Harbo, Ingrid, et al, 1998. "Asymptotic Inference on Cointegrating Rank in Partial Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 388-399, October.
    5. Jing Li & Junsoo Lee, 2010. "ADL tests for threshold cointegration," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 241-254, July.
    6. Zivot, Eric, 2000. "The Power Of Single Equation Tests For Cointegration When The Cointegrating Vector Is Prespecified," Econometric Theory, Cambridge University Press, vol. 16(3), pages 407-439, June.
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    Cited by:

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