Abstract
While traditionally considered for non-stationary and cointegrated data, De Boef and Keele (2008) suggest applying a General Error Correction Model to stationary data with or without cointegration. The GECM has since become extremely popu- lar 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 se- ries 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.
Published Version
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