Multivariate regression analysis of panel data with binary outcomes applied to unemployment data |
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Authors: | Claudia Czado |
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Institution: | (1) Department of Mathematics and Statistics, York University, 4700 Keele Street, M3J 1P3 North York, Ontario, Canada |
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Abstract: | Summary In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected
over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among
the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for
multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure
for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist
applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this
paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11
waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution
(see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification
which fit the data well.
C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada. |
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Keywords: | Binary time series panel data multivariate probit model Bayesian analysis Markov Chain Monte Carlo methods Bayesian model checking posterior predictive distribution |
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