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To Lag or Not to Lag?: Re-Evaluating the Use of Lagged Dependent Variables in Regression Analysis*

Published online by Cambridge University Press:  03 May 2017

Arjun S. Wilkins
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Abstract

Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This actually implies that more LDV and lagged independent variables should be included in the specification, not fewer. Including the additional lags yields more accurate parameter estimates, which I demonstrate using the same data-generating process scholars had previously used to argue against including LDVs. I use Monte Carlo simulations to show that this specification returns much more accurate coefficient estimates for independent variables (across a wide range of parameter values) than alternatives considered in earlier research. The simulation results also indicate that improper exclusion of LDVs can lead to severe bias in coefficient estimates. While no panacea, scholars should continue to confidently include LDVs as part of a robust estimation strategy.

Type
Research Notes
Information
Political Science Research and Methods , Volume 6 , Issue 2 , April 2018 , pp. 393 - 411
Copyright
© The European Political Science Association 2017 

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Footnotes

*

Arjun S. Wilkins, Department of Political Science, Stanford University, Encina Hall West, Room 100, 616 Serra St., Stanford, CA 94305-6044 (arjun.wilkins@gmail.com). I wish to thank Justin Grimmer, Simon Jackman, Bobby Gulotty, and two anonymous reviewers for their very helpful comments and advice as I worked on this paper. Any errors or omissions are the author’s responsibility. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2017.4

References

Achen, Christopher H. 2000. ‘Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables’. Presented at the Annual Meeting of the Political Methodology Section of the American Political Science Association, July 20–22, Los Angeles.Google Scholar
Beck, Nathaniel. 1991. ‘Comparing Dynamic Specifications: The Case of Presidential Approval’. Political Analysis 3(1):51–87.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N.. 2011. ‘Modeling Dynamics in Time-Series-Cross-Section Political Economy Data’. Annual Review of Political Science 14:331–352.Google Scholar
Canes-Wrone, Brandice, Brady, David W., and Cogan, John F.. 2002. ‘Out of Step, Out of Office: Electoral Accountability and House Members’ Voting’. American Political Science Review 96(1):127–140.Google Scholar
Carson, Jamie L., Koger, Gregory, Lebo, Matthew J., and Young, Everett. 2010. ‘The Electoral Costs of Party Loyalty in Congress’. American Journal of Political Science 54(3):598–616.Google Scholar
Caselli, Francesco, Esquivel, Gerardo, and Lefort, Fernando. 1996. ‘Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics’. Journal of Economic Growth 1(3):363–389.CrossRefGoogle Scholar
Cerrito, Patricia B. 1992. ‘Predicting Wolf’s Sunspot Numbers With and Without the Assumption of Periodicity’. The Astrophysical Journal 393(2):795–799.Google Scholar
Cox, Gary W., and Katz, Jonathan N.. 1996. ‘Why Did the Incumbency Advantage in U.S. House Elections Grow?’. American Journal of Political Science 40(2):478–497.CrossRefGoogle Scholar
De Boef, Suzanna, and Keele, Luke. 2008. ‘Taking Time Seriously’. American Journal of Political Science 52(1):184–200.CrossRefGoogle Scholar
El-Din, Ahmed Gamal, and Smith, Daniel W.. 2002. ‘A Combined Transfer-Function Noise Model to Predict the Dynamic Behavior of a Full-Scale Primary Sedimentation Tank’. Water Research 36(15):3747–3764.Google Scholar
Evans, Geoffrey, and Pickup, Mark. 2010. ‘Reversing the Causal Arrow: The Political Conditioning of Economic Perceptions in the 2000-2004 U.S. Presidential Cycle’. Journal of Politics 72(4):1236–1251.Google Scholar
Garrett, Geoffrey, and Mitchell, Deborah. 2001. ‘Globalization, Government Spending, and Taxation in the OECD’. European Journal of Political Research 39(2):145–177.CrossRefGoogle Scholar
Gelman, Andrew, and King, Gary. 1990. ‘Estimating Incumbency Advantage Without Bias’. American Journal of Political Science 34(4):1141–1164.CrossRefGoogle Scholar
Green, Donald, Palmquist, Bradley, and Schickler, Eric. 1998. ‘Macropartisanship: A Replication and Critique’. The American Political Science Review 92(4):883–899.CrossRefGoogle Scholar
Hendry, David F. 1995. Dynamic Econometrics. Oxford: Oxford University Press.Google Scholar
Huber, Evelyne, and Stephens, John D.. 2001. Development and Crisis of the Welfare State. Chicago, IL: University of Chicago Press.Google Scholar
Hurwicz, Leonid. 1950. ‘Least Squares Bias in Time Series’. In Tjalling C. Koopmans (ed.), Statistical Inference in Dynamic Economic Models , 365–383. New York: Wiley.Google Scholar
Jacobson, Gary C. 1996. ‘The 1994 House Elections in Perspective’. Political Science Quarterly 111(2):203–223.Google Scholar
Keele, Luke, and Kelly, Nathan J.. 2006. ‘Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables’. Political Analysis 14(2):186–205.Google Scholar
Lewis-Beck, Michael S. 2006. ‘Does Economics Still Matter? Econometrics and the Vote’. Journal of Politics 68(1):208–212.Google Scholar
Lewis-Beck, Michael S., Nadeau, Richard, and Elias, Angelo. 2008. ‘Economics, Party, and the Vote: Causality Issues and Panel Data’. American Journal of Political Science 52(1):84–95.Google Scholar
Montanari, Alberto, Rosso, Renzo, and Taqqu, Murad S.. 2000. ‘A Seasonal Fractional ARIMA Model Applied to the Nile River Monthly Flows at Aswan’. Water Resources Research 36(5):1249–1259.Google Scholar
Morgan, Stephen L., and Winship, Christopher. 2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research. New York: Cambridge University Press.CrossRefGoogle Scholar
Nickell, Stephen. 1981. ‘Bias in Dynamic Models With Fixed Effects’. Econometrica 49(6):1417–1426.Google Scholar
Plümper, Thomas, Troeger, Vera E., and Manow, Philip. 2005. ‘Panel Data Analysis in Comparative Politics: Linking Method to Theory’. European Journal of Political Research 44(2):327–354.Google Scholar
Shumway, Robert H., and Stoffer, David S.. 2006. Time Series Analysis and its Applications. New York: Springer.Google Scholar
Singh, Manjushree, Singh, Rajendra, and Shinde, Vipul. 2011. ‘Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India’. International Journal of Computer Applications 20(3):7–14.CrossRefGoogle Scholar
Wei, William W. S. 2005. Time Series Analysis: Univariate and Multivariate Methods 2nd ed. New York: Pearson.Google Scholar
Wooldridge, Jeffrey. 2012. Introductory Econometrics: A Modern Approach 5th ed. Stamford: Cengage Learning.Google Scholar
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Dataset

http://dx.doi.org/10.7910/DVN/UERKTM
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