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Error Correction Methods with Political Time Series

  • Taylor Grant (a1) and Matthew J. Lebo (a2)

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.

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Authors' note: Previous versions of this article were presented at the 2014 meeting of the Midwest Political Science Association, the 2014 meeting of the European Political Science Association, and the 2014 PolMeth conference. We are grateful for helpful comments from Neal Beck, Matthew Blackwell, Janet Box-Steffensmeier, Patrick Brandt, Justin Esarey, Stanley Feldman, Paul Kellstedt, Suzanna Linn, Vera Troeger, and seminar participants at Rice University and Stony Brook University. Replication materials are available online as Grant and Lebo (2016). Supplementary materials for this article are available on the Political Analysis Web site and at:

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