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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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    Cited by:

    1. Bosupeng, Mpho, 2017. "Electricity Consumption and Exports Growth: Revisiting the Feedback Hypothesis," MPRA Paper 81756, University Library of Munich, Germany, revised 2017.
    2. 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.
    3. Mpho Bosupeng, 2015. "The Fisher Effect Using Differences in The Deterministic Term," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(4), pages 1031-1031.
    4. Bosupeng, Mpho, 2015. "On Exports and Economic Growth-Multifarious Economies Perspective," MPRA Paper 77922, University Library of Munich, Germany, revised 2015.
    5. Eroğlu, Burak Alparslan, 2019. "Wavelet variance ratio cointegration test and wavestrapping," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 298-319.
    6. Daniel Greenfield & Bruce Kobayashi & Jeremy Sandford & Christopher Taylor & Nathan Wilson, 2019. "Economics at the FTC: Quantitative Analyses of Two Chemical Manufacturing Mergers," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(4), pages 607-623, December.
    7. 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.
    8. Jason R. V. Franken & Scott H. Irwin, 2024. "Revisiting biodiesel hedging," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 1002-1015, October.
    9. 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.
    10. 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.
    11. Burak Eroglu & Kemal Caglar Gogebakan & Mirza Trokic, 2017. "Fractional Seasonal Variance Ratio Unit Root Tests," Working Papers 1707, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    12. Franken, Jason R.V. & Irwin, Scott H. & Garcia, Philip, 2021. "Biodiesel hedging under binding renewable fuel standard mandates," Energy Economics, Elsevier, vol. 96(C).

    More about this item

    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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