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Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables

Published online by Cambridge University Press:  04 January 2017

Luke Keele
Affiliation:
Department of Political Science, Ohio State University, 154 N. Oval Mall, Columbus, OH 43210. e-mail: luke.keele@mail.polisci.ohio-state.edu (corresponding author)
Nathan J. Kelly
Affiliation:
Department of Political Science, University of Tennessee, 1001 McClung Tower, Knoxville, TN 37996–0410. e-mail: nathan.j.kelly@gmail.com

Abstract

A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effects in political processes and as a method for ridding the model of autocorrelation. But recent work contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residual autocorrelation is present, the lagged dependent variable causes the coefficients for explanatory variables to be biased downward. We use a Monte Carlo analysis to assess empirically how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. In our analysis, we compare the performance of the lagged dependent variable model to several other time series models. We show that while the lagged dependent variable is inappropriate in some circumstances, it remains an appropriate model for the dynamic theories often tested by applied analysts. From the analysis, we develop several practical suggestions on when and how to use lagged dependent variables on the right-hand side of a model.

Type
Research Article
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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