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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  and
Nathan J. Kelly
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
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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
Information
Political Analysis , Volume 14 , Issue 2 , Spring 2006 , pp. 186 - 205
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Achen, Christopher H. 2000. “Why Lagged Dependent Variables Can Supress the Explanatory Power of Other Independent Variables.” Presented at the Annual Meeting of Political Methodology, Los Angeles.Google Scholar
Beck, Nathaniel. 1985. “Estimating Dynamic Models Is Not Merely a Matter of Technique.” Political Methodology 11: 71–89.Google Scholar
Beck, Nathaniel. 1992. “Comparing Dynamic Specifications: The Case of Presidential Approval.” Political Analysis 3: 27–50.Google Scholar
Davidson, Russell, and MacKinnon, James G. 1993. Estimation and Inference in Econometrics. New York: Oxford University Press.Google Scholar
Greene, William H. 2003. Econometric Analysis, 5th ed. New York: Macmillan.Google Scholar
Griliches, Zvi. 1961. “A Note of Serial Correlation Bias in Estimates of Distributed Lags.” Econometrica 29: 65–73.CrossRefGoogle Scholar
Hendry, David F. 1995. Dynamic Econometrics. Oxford: Oxford University Press.Google Scholar
Hendry, David, and Mizon, Grayham. 1978. “Serial Correlation as a Convenient Simplification, Not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England.” Economic Journal 88: 549–563.Google Scholar
Hibbs, Douglas A. Jr. 1974. “Problems of Statistical Estimation and Causal Inference in Time-Series Regression Models.” In Sociological Methodology 1973–1974, ed. Costner, Herbert L. San Francisco: Jossey-Bass, pp. 252–308.Google Scholar
Hurwicz, L. 1950. “Least-Squares Bias in Time Series.” In Statistical Inference in Dynamic Economic Models, ed. Koopmans, T. New York: Wiley, pp. 215–249.Google Scholar
Maddala, G. S., and Rao, A. S. 1973. “Tests for Serial Correlation in Regression Models with Lagged Dependent Variables and Serially Correlated Errors.” Econometrica 47: 761–774.CrossRefGoogle Scholar
Malinvaud, E. 1970. Statistical Methods of Econometrics, 2nd ed. Amsterdam: North-Holland.Google Scholar
Mizon, Grayham. 1995. “A Simple Message for Autocorrelation Correctors—Don't.” Journal of Econometrics 69: 267–288.Google Scholar
Phillips, P. C. B. 1977. “Approximations to Some Finite Sample Distributions Associated with a First-Order Stochatic Difference Equation.” Econometrica 45: 463–486.Google Scholar
Phillips, P. C. B., and Wickens, M. R. 1978. Exercises in Econometrics, vol. 2. Oxford: Phillip Allan.Google Scholar
White, J. 1961. “Asymptotic Expansions for the Mean and Variance of the Serial Correlation Coefficient.” Biometrika 48: 85–94.Google Scholar
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