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Title: Replication data for: Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution      
dateReleased:
01-21-2009
downloadURL: http://hdl.handle.net/1902.1/10209
ID:
hdl:1902.1/10209
description:
This article analyzes the consequences of nonrandom 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 (nonexistent) duration dependence. In addition, our proposed estimator is found to be superior in root mean-square error terms when nontrivial amounts of selection are present. 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.
description:
Frederick J. Boehmke; Daniel S. Morey; and Megan Shannon, 2009, "Replication data for: Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution", http://hdl.handle.net/1902.1/10209, Harvard Dataverse, V1
name:
Frederick J. Boehmke
Daniel S. Morey
and Megan Shannon
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997