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Testing for Cointegration in the Presence of Moving Average Errors

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  • Mallory, Mindy
  • Lence, Sergio H

Abstract

This study explores performance of the Johansen cointegration statistics on data containing negative moving average (NMA) errors. Monte Carlo experiments demonstrate that the asymptotic distributions of the statistics are sensitive to NMA parameters, and that using the standard 5% asymptotic critical values results in severe underestimation of the actual test sizes. We demonstrate that problems associated with NMA errors do not decrease as sample size increases; instead, they become more severe. Further we examine evidence that many U.S. commodity prices are characterized by NMA errors. Pretesting data is recommended before using standard asymptotic critical values for Johansen’s cointegration tests.

Suggested Citation

  • Mallory, Mindy & Lence, Sergio H, 2012. "Testing for Cointegration in the Presence of Moving Average Errors," ISU General Staff Papers 201201010800001034, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:201201010800001034
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    3. Mallory Mindy & Lence Sergio H., 2012. "Testing for Cointegration in the Presence of Moving Average Errors," Journal of Time Series Econometrics, De Gruyter, vol. 4(2), pages 1-68, November.
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    6. Burak Eroglu, 2017. "Wavelet Variance Ratio Test And Wavestrapping For The Determination Of The Cointegration Rank," Working Papers 1706, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    7. Osabuohien-Irabor Osarumwense & Julian I. Mbegbu, 2017. "Power and Size analysis of Co-integration tests in Conditional Heteroskedascity: A Monte Carlo Simulation," Romanian Statistical Review, Romanian Statistical Review, vol. 65(3), pages 17-34, September.
    8. Bosupeng, Mpho, 2015. "The Impossible Trinity and Financial Markets – An Examination of Inflation Volatility Spillovers," MPRA Paper 77923, University Library of Munich, Germany, revised 2015.
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    JEL classification:

    • 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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