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What To Do About Atheoretic Lags*

Published online by Cambridge University Press:  26 August 2015

Skyler J. Cranmer ,
Douglas R. Rice  and
Randolph M. Siverson
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Abstract

We examine a problem that is confronted frequently by political science researchers seeking to model longitudinal data: what to do when one suspects a lag between the realization of a regressor and its effect on the outcome variable, but one has no theoretical reason to suspect a particular lag length. We examine the theoretical challenges posed by atheoretic lags, review existing methods for atheoretic lag analysis—most notably distributed lag specifications—and their shortcomings, and present an alternative approach for atheoretic lag analysis based on Bayesian model averaging (BMA). We demonstrate the use and utility of our approach with two examples: the litigant signal model in American politics and modernization theory in political economy. Our examples show the increasing difficulty of analyzing models with atheoretic lags as the set of possible specifications increases, and demonstrate the effectiveness of BMA for the modal type of specification in time-series cross-sectional applications.

Type
Original Articles
Information
Political Science Research and Methods , Volume 5 , Issue 4 , October 2017 , pp. 641 - 665
Copyright
© The European Political Science Association 2015 

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Footnotes

*

Skyler Cranmer, Carter Phillips and Sue Henry Associate Professor, Department of Political Science, The Ohio State University, 154 North Oval Mall, Derby Hall 2032, Columbus, OH 43210 (cranmer.12@osu.edu). Douglas Rice, Assistant Professor, Department of Political Science, University of Mississippi, P.O. Box 1848, 228 Deupree Hall University, MS 38677-1848 (drrice@olemiss.edu). Randolph Siverson, Distinguished Professor Emeritus, Department of Political Science, University of California, Davis 578 Kerr Hall, One Shields Ave. Davis, CA 95616 (rmsiverson@ucdavis.edu). Thanks are due to Janet Box-Steffensmeier, James Fowler, Jeff Gill, Domonik Hangartner, Gary King, Lee Walker, and Chris Zorn for their helpful comments. SJC gratefully acknowledges the support of the National Science Foundation (SES-1461493 and SES-1357622) and the Alexander von Humboldt Foundation.

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