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Addressing Monotone Likelihood in Duration Modelling of Political Events

Published online by Cambridge University Press:  29 June 2020

Noel Anderson Open the ORCID record for Noel Anderson [Opens in a new window] ,
Benjamin E. Bagozzi  and
Ore Koren
Noel Anderson*
Affiliation:
Department of Political Science, University of Toronto, Canada
Benjamin E. Bagozzi
Affiliation:
Deparment of Political Science and International Relations, University of Delaware, USA
Ore Koren
Affiliation:
Department of Political Science, Indiana University, Bloomington, IN, USA
*
*Corresponding author. E-mail: noel.anderson@utoronto.ca
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Abstract

This article provides an accessible introduction to the phenomenon of monotone likelihood in duration modeling of political events. Monotone likelihood arises when covariate values are monotonic when ordered according to failure time, causing parameter estimates to diverge toward infinity. Within political science duration model applications, this problem leads to misinterpretation, model misspecification and omitted variable biases, among other issues. Using a combination of mathematical exposition, Monte Carlo simulations and empirical applications, this article illustrates the advantages of Firth's penalized maximum-likelihood estimation in resolving the methodological complications underlying monotone likelihood. The results identify the conditions under which monotone likelihood is most acute and provide guidance for political scientists applying duration modeling techniques in their empirical research.

Keywords

duration modelsmonotone likelihoodpenalized maximum likelihoodexternal interventionstate partition
Type
Article
Information
British Journal of Political Science , Volume 51 , Issue 4 , October 2021 , pp. 1654 - 1671
Copyright
Copyright © Cambridge University Press 2020

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