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

Nathan J. Kelly Department of Political Science, University of Tennessee, 1001 McClung Tower, Knoxville, TN 37996-0410 e-mail: luke.keele{at}mail.polisci.ohio-state.edu (corresponding author) e-mail: nathan.j.kelly{at}gmail.com A lagged dependent variable in an OLS regression is often usedas a means of capturing dynamic effects in political processesand as a method for ridding the model of autocorrelation. Butrecent work contends that the lagged dependent variable specificationis too problematic for use in most situations. More specifically,if residual autocorrelation is present, the lagged dependentvariable causes the coefficients for explanatory variables tobe biased downward. We use a Monte Carlo analysis to assessempirically how much bias is present when a lagged dependentvariable is used under a wide variety of circumstances. In ouranalysis, we compare the performance of the lagged dependentvariable model to several other time series models. We showthat while the lagged dependent variable is inappropriate insome circumstances, it remains an appropriate model for thedynamic theories often tested by applied analysts. From theanalysis, we develop several practical suggestions on when andhow to use lagged dependent variables on the right-hand sideof a model.