Skip to main content
    • Aa
    • Aa

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


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.

Hide All

Arjun S. Wilkins, Department of Political Science, Stanford University, Encina Hall West, Room 100, 616 Serra St., Stanford, CA 94305-6044 ( 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

Hide All
Nathaniel Beck . 1991. ‘Comparing Dynamic Specifications: The Case of Presidential Approval’. Political Analysis 3(1):5187.

Nathaniel Beck , and Jonathan N. Katz . 2011. ‘Modeling Dynamics in Time-Series-Cross-Section Political Economy Data’. Annual Review of Political Science 14:331352.

Jamie L. Carson , Gregory Koger , Matthew J. Lebo , and Everett Young . 2010. ‘The Electoral Costs of Party Loyalty in Congress’. American Journal of Political Science 54(3):598616.

Patricia B Cerrito . 1992. ‘Predicting Wolf’s Sunspot Numbers With and Without the Assumption of Periodicity’. The Astrophysical Journal 393(2):795799.

Gary W. Cox , and Jonathan N. Katz . 1996. ‘Why Did the Incumbency Advantage in U.S. House Elections Grow?’. American Journal of Political Science 40(2):478497.

Suzanna De Boef , and Luke Keele . 2008. ‘Taking Time Seriously’. American Journal of Political Science 52(1):184200.

Ahmed Gamal El-Din , and Daniel W. Smith . 2002. ‘A Combined Transfer-Function Noise Model to Predict the Dynamic Behavior of a Full-Scale Primary Sedimentation Tank’. Water Research 36(15):37473764.

Geoffrey Garrett , and Deborah Mitchell . 2001. ‘Globalization, Government Spending, and Taxation in the OECD’. European Journal of Political Research 39(2):145177.

David F Hendry . 1995. Dynamic Econometrics. Oxford: Oxford University Press.

Michael S Lewis-Beck . 2006. ‘Does Economics Still Matter? Econometrics and the Vote’. Journal of Politics 68(1):208212.

Stephen L. Morgan , and Christopher Winship . 2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research. New York: Cambridge University Press.

Thomas Plümper , Vera E. Troeger , and Philip Manow . 2005. ‘Panel Data Analysis in Comparative Politics: Linking Method to Theory’. European Journal of Political Research 44(2):327354.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Science Research and Methods
  • ISSN: 2049-8470
  • EISSN: 2049-8489
  • URL: /core/journals/political-science-research-and-methods
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
Type Description Title
Supplementary Materials

Wilkins supplementary material

Supplementary Materials

Wilkins supplementary material

 PDF (783 KB)
783 KB


Full text views

Total number of HTML views: 6
Total number of PDF views: 113 *
Loading metrics...

Abstract views

Total abstract views: 416 *
Loading metrics...

* Views captured on Cambridge Core between 3rd May 2017 - 19th October 2017. This data will be updated every 24 hours.