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Error Correction Methods with Political Time Series

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  • Grant, Taylor
  • Lebo, Matthew J.

Abstract

While traditionally considered for non-stationary and cointegrated data, DeBoef and Keele suggest applying a General Error Correction Model (GECM) to stationary data with or without cointegration. The GECM has since become extremely popular in political science but practitioners have confused essential points. For one, the model is treated as perfectly flexible when, in fact, the opposite is true. Time series of various orders of integration–stationary, non-stationary, explosive, near- and fractionally integrated–should not be analyzed together but researchers consistently make this mistake. That is, without equation balance the model is misspecified and hypothesis tests and long-run-multipliers are unreliable. Another problem is that the error correction term's sampling distribution moves dramatically depending upon the order of integration, sample size, number of covariates, and the boundedness of Yt. This means that practitioners are likely to overstate evidence of error correction, especially when using a traditional t-test. We evaluate common GECM practices with six types of data, 746 simulations, and five paper replications.

Suggested Citation

  • Grant, Taylor & Lebo, Matthew J., 2016. "Error Correction Methods with Political Time Series," Political Analysis, Cambridge University Press, vol. 24(1), pages 3-30, January.
    • Handle: RePEc:cup:polals:v:24:y:2016:i:1:p:3-30_2
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    Cited by:

    1. Bernhard Reinsberg & Thomas Stubbs & Alexander Kentikelenis & Lawrence King, 2020. "Bad governance: How privatization increases corruption in the developing world," Regulation & Governance, John Wiley & Sons, vol. 14(4), pages 698-717, October.
    2. Anthony J. McGann & Sebastian Dellepiane‐Avellaneda & John Bartle, 2023. "Dynamics of public opinion and policy response under proportional and plurality elections," Economics and Politics, Wiley Blackwell, vol. 35(1), pages 333-355, March.
    3. Santiago López-Cariboni & Xun Cao, 2019. "When do authoritarian rulers educate: Trade competition and human capital investment in Non-Democracies," The Review of International Organizations, Springer, vol. 14(3), pages 367-405, September.
    4. Bakker, Vincent & Van Vliet, Olaf, 2019. "Social Investment, Employment Outcomes and Policy and Institutional Complementarities: A Comparative Analysis across 26 OECD countries," MPRA Paper 96140, University Library of Munich, Germany.
    5. Abla A. H. Bokhari, 2017. "Human Capital Investment and Economic Growth in Saudi Arabia: Error Correction Model," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 104-112.
    6. Ali Hassan Shabbir & Jiquan Zhang & James D Johnston & Samuel Asumadu Sarkodie & James A Lutz & Xingpeng Liu, 2020. "Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-19, April.
    7. Chletsos, Michael & Sintos, Andreas, 2021. "Hide and seek: IMF intervention and the shadow economy," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 292-319.

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