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More powerful cointegration tests with non-normal errors

Author

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  • Lee Hyejin

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

  • 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.
    3. Banerjee, Anindya, et al, 1986. "Exploring Equilibrium Relationships in Econometrics through Static Models: Some Monte Carlo Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 253-277, August.
    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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    5. Dong-Yop Oh & Hyejin Lee & Karl David Boulware, 2016. "Conventional monetary policy and the degree of interest rate pass through in the long run: a non-normal approach," Wesleyan Economics Working Papers 2016-002, Wesleyan University, Department of Economics.
    6. Banerjee, Piyali & Arčabić, Vladimir & Lee, Hyejin, 2017. "Fourier ADL cointegration test to approximate smooth breaks with new evidence from Crude Oil Market," Economic Modelling, Elsevier, vol. 67(C), pages 114-124.
    7. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
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    More about this item

    Keywords

    cointegration; non-normal errors; nonlinear processes; RALS;
    All these keywords.

    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

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