Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution

Frederick J. Boehmke, University of Iowa
Daniel S. Morey
Megan Shannon

Abstract

This paper analyzes the consequences of non-random sample selection for continuous-time duration analyses and develops a new estimator to correct for it when necessary. We conduct a series of Monte Carlo analyses that estimate common duration models as well as our proposed duration model with selection. These simulations show that ignoring sample selection issues can lead to biased parameter estimates, including the appearance of (non-existent) duration dependence. In addition, our proposed estimator is found to be superior in root mean square error terms with non-trivial amounts of selection. Finally, we provide an empirical application of our method by studying whether self-selectivity is a problem for studies of leaders’ survival during and following militarized conflicts.