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Accounting for Right Censoring in Interdependent Duration Analysis

Published online by Cambridge University Press:  04 January 2017

Jude C. Hays ,
Emily U. Schilling  and
Frederick J. Boehmke
Jude C. Hays*
Affiliation:
University of Pittsburgh, 4600 Wesley W. Posvar Hall, Pittsburgh, PA 15260
Emily U. Schilling
Affiliation:
University of Iowa, 341 Schaeffer Hall, Iowa City, IA 52242. e-mail: emily-schilling@uiowa.edu
Frederick J. Boehmke
Affiliation:
University of Iowa, 341 Schaeffer Hall, Iowa City, IA 52242. e-mail: frederick-boehmke@uiowa.edu
*
e-mail: jch61@pitt.edu (corresponding author)
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Abstract

Duration data are often subject to various forms of censoring that require adaptations of the likelihood function to properly capture the data generating process, but existing spatial duration models do not yet account for these potential issues. Here, we develop a method to estimate spatial-lag duration models when the outcome suffers from right censoring, the most common form of censoring. We adapt Wei and Tanner's (1991) imputation algorithm for censored (non-spatial) regression data to models of spatially interdependent durations. The algorithm treats the unobserved duration outcomes as censored data and iterates between multiple imputation of the incomplete, that is, right censored, values and estimation of the spatial duration model using these imputed values. We explore the performance of an estimator for log-normal durations in the face of varying degrees of right censoring via Monte Carlo and provide empirical examples of its estimation by analyzing spatial dependence in states' entry dates into World War I.

Type
Articles
Information
Political Analysis , Volume 23 , Issue 3 , Summer 2015 , pp. 400 - 414
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We are grateful for funding provided by the University of Iowa Department of Political Science. Comments from Chris Zorn, Justin Grimmer, Matthew Blackwell, and participants at a University of Rochester seminar are gratefully acknowledged. Replication materials for this study are available from the Political Analysis Dataverse at http://dx.doi.org/10.7910/DVN/29603. Supplementary materials for this article are available on the Political Analysis Web site.

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