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Estimating Onsets of Binary Events in Panel Data

Published online by Cambridge University Press:  04 January 2017

Liam F. McGrath*
Centre for Comparative and International Studies (CIS) and Institute for Environmental Decisions (IED), ETH Zürich, Switzerland
e-mail: (corresponding author)


Onsets of binary events are often of interest to political scientists, whether they be regime changes, the occurrence of civil war, or the signing of bilateral agreements, to name a few. Often researchers transform the binary event outcome of interest, by setting ongoing years to zero, to create a variable which measures the onset of the event. While this may seem an intuitive way to go about estimating models where onset is the outcome of interest, it results in two problems that can affect substantive inferences. First, it creates two qualitatively different meanings for a unit time period to have a zero, which estimators are unable to “know.” Second, it ignores the possibility that variables may have differing effects upon binary event onsets and durations. This article explores how much this transformation can harm our substantive inferences by analytically demonstrating the resulting bias and the use of Monte Carlo experiments, as well as offering recommendations to avoid these problems. I also conduct a sensitivity analysis on the determinants of civil war onset to examine how substantive inferences are affected by this issue. In doing so, I find that there is considerable difference in the size of estimated coefficients and whether a variable is considered a robust determinant of civil war.

Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Author's note: Thanks to Janina Beiser, Kevin Clarke, Patrick Kuhn, Thomas Plümper, Curtis Signorino, Janne Tukiainen, Robert Walker, Julian Wucherpfennig, and Christopher Zorn, the Editor, and the anonymous reviewers for comments and suggestions. Replication materials are available at Supplementary materials for this article are available on the Political Analysis Web site.


Bane, Mary Jo, and Ellwood, David T. 1986. Slipping into and out of poverty. Journal of Human Resources 21:223.Google Scholar
Barmby, Tim. 1998. The relationship between event history and discrete time duration models: An application to the analysis of personnel absenteeism. Oxford Bulletin of Economics and Statistics 60:261–65.Google Scholar
Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard. 1998. Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42(4): 1260–88.Google Scholar
Beck, Nathaniell, Epstein, Simon Jackman, David, and O’Halloran, Sharyn. 2001. Alternative models of dynamics in binary time-series-cross-section models: The example of state failure. Working paper.Google Scholar
Bergholt, D., and Lujala, P. 2012. Climate-related natural disasters, economic growth, and armed civil conflict. Journal of Peace Research 49(1): 147–62.Google Scholar
Boskin, M. J., and Nold, F. C. 1975. A Markov model of turnover in aid to families with dependent children. Journal of Human Resources 10:476–81.Google Scholar
Carter, David B., and Signorino, Curtis S. 2010. Back to the future: Modeling time dependence in binary data. Political Analysis 18(3): 271–92.Google Scholar
Diggle, Peter, Liang, Kung-Yee, and Zeger, Scott L. 1994. Analysis of longitudinal data. Oxford: Oxford University Press.Google Scholar
Fearon, James D., and Laitin, David D. 2003. Ethnicity, insurgency, and civil war. American Political Science Review 97(1): 7590.Google Scholar
Getmansky, Anna. 2012. You can't win if you don't fight: The role of regime type in counterinsurgency outbreaks and outcomes. Journal of Conflict Resolution 57:709–34.Google Scholar
Hegre, Havard, and Sambanis, Nicholas. 2006. Sensitivity analysis of empirical results on civil war onset. Journal of Conflict Resolution 50:508–36.Google Scholar
Jackman, Simon. 2000. In and out of war and peace: Transitional models of international conflict. Working paper.Google Scholar
King, Gary, and Zeng, Lanche. 2001. Explaining rare events in international relations. International Organization 55(3): 693715.Google Scholar
McGrath, Liam F. 2015. Replication data for: Estimating onsets of binary events in panel data. Harvard Dataverse, V1 [UNF:6:QIfNzWwGaK+slGPMJKjf+w==] Scholar
Meyer, Bruce, and Mittag, Nikolas. 2013. Misclassification in binary choice models. Working paper.Google Scholar
Przeworski, Adam, and Raymond Vreeland, James. 2002. A statistical model of bilateral cooperation. Political Analysis 10(2): 101–12.CrossRefGoogle Scholar
Przeworski, Adam, Alvarez, Michael E., Antonio Cheibub, Jose, and Limongi, Fernando. 2000. Democracy and development: Political institutions and well-being in the world, 1950–1990. Cambridge, UK: Cambridge University Press.Google Scholar
Sala-I-Martin, Xavier X. 1997. I just ran two million regressions. American Economic Review 87(2): 178–83.Google Scholar
Yamaguchi, Kazuo. 1991. Event history analysis. Vol. 28 of Applied Social Research Methods Series. Newbury Park, CA: Sage.Google Scholar
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