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Selection Bias and Continuous-Time Duration Models
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Abstract
In this paper we explore the consequences of non-random sample selection for continuous time duration analysis. While the consequences of selectivity are reasonably well-understood in linear regression and common discrete choice models, we have little or no understanding of how selectivity affects duration models. We study this issue by conducting a series of Monte Carlo analyses that estimate common duration models on data that suffer from selectivity. Our findings indicate that the consequences are severe: both coefficients and standard errors may be biased in an unknown direction. In addition, we find that selection bias may create the appearance of (non-existent) duration dependence. Given these difficulties, we develop a solution for self-selectivity bias in duration models and apply this solution to a prominent study of war participation and political leadership tenure. Based on empirical analysis and Monte Carlo simulations, our solution for selectivity bias in duration models is superior to models that ignore the problem.
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| | Authors: Boehmke, Frederick., Morey, Daniel. and Shannon, Megan. |
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2
Abstract
In this paper we explore the consequences of non-random sample selection
for continuous time duration analysis. While the consequences of
selectivity are reasonably well-understood in linear regression and
common discrete choice models, we have little or no understanding of
how selectivity affects duration models. We study this issue by
conducting a series of Monte Carlo analyses that estimate common
duration models on data that suffer from selectivity. Our findings indicate
that the consequences are severe: both coefficients and standard errors
may be biased in an unknown direction. In addition, we find that selection
bias may create the appearance of (non-existent) duration dependence.
Given these difficulties, we develop a solution for self-selectivity bias in
duration models and apply this solution to a prominent study of war
participation and political leadership tenure. Based on empirical analysis
and Monte Carlo simulations, our solution for selectivity bias in duration
models is superior to models that ignore the problem.
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