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Introduction to Symposium on Time Series Error Correction Methods in Political Science

Published online by Cambridge University Press:  04 January 2017

Janet Box-Steffensmeier*
Affiliation:
Department of Political Science, Ohio State University, 2140 Derby Hall, 154 N. Oval Mall, Columbus, OH 43210-1373
Agnar Freyr Helgason
Affiliation:
Social Research Centre, University of Iceland, Gimli G-201, Sæmundargötu 2, 101 Reykjavík, Iceland, e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)

Extract

In recent years, political science has seen a boom in the use of sophisticated methodological tools for time series analysis. One such tool is the general error correction model (GECM), originally introduced to political scientists in the pages of this journal over 20 years ago (Durr 1992; Ostrom and Smith 1992) and re-introduced by De Boef and Keele (2008), who advocate its use for a wider set of time series data than previously considered appropriate. Their article has proven quite influential, with numerous papers justifying their methodological choices with reference to De Boef and Keele's contribution.

Grant and Lebo (2016) take issue with the increasing use of the GECM in political science and argue that the methodology is widely misused and abused by practitioners. Given the recent surge of research conducted using error correction methods, there is every reason to take their suggestions seriously and provide a fuller discussion of the points they raise in their paper. The present symposium serves such a role. It consists of Grant and Lebo's critique, a detailed response by Keele, Linn, and Webb (2016b), and shorter comments by Esarey (2016), Freeman (2016), and Helgason (2016). Finally, Lebo and Grant (2016) and Keele, Linn, and Webb (2016a) reflect on the contributions made in the symposium, as well as discuss outstanding issues.

Type
Time Series Symposium
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Box-Steffensmeier, Janet M., Freeman, John, Hitt, Matthew, and Pevehouse, Jon. 2014. Time Series Analysis for the Social Sciences. New York: Cambridge University Press.Google Scholar
De Boef, Suzanna, and Keele, Luke. 2008. Taking time seriously. American Journal of Political Science 52(1): 184200.Google Scholar
Durr, Robert H. 1992. An essay on cointegration and error correction models. Political Analysis 4:185227.Google Scholar
Esarey, Justin. 2016. Fractionally integrated data and the autodistributed lag model: Results from a simulation study. Political Analysis 24:4249.CrossRefGoogle Scholar
Freeman, John. 2016. Progress in the study of nonstationary political time series? Political Analysis 24:5058.CrossRefGoogle Scholar
Grant, Taylor, and Lebo, Matthew J. 2016. Error correction methods with political time series. Political Analysis 24:330.Google Scholar
Helgason, Agnar Freyr. 2016. Fractional integration methods and short time series: Evidence from a simulation study. Political Analysis 24:5968.Google Scholar
Keele, Luke, Linn, Suzanna, and Webb, Clayton. 2016a. Taking time with all due seriousness. Political Analysis 24:3141.Google Scholar
Keele, Luke, Linn, Suzanna, and Webb, Clayton. 2016b. Concluding comments. Political Analysis 24:8386.CrossRefGoogle Scholar
Lebo, Matthew J., and Grant, Taylor. 2016. Equation balance and dynamic political modeling. Political Analysis 24:6982.Google Scholar
Ostrom, Charles W., and Smith, Renee M. 1992. Error correction, attitude persistence, and executive rewards and punishments: A behavioral theory of presidential approval. Political Analysis 4:127–83.CrossRefGoogle Scholar