Multivariate regression analysis of panel data with binary outcomes applied to unemployment data
Multivariate regression analysis of panel data with binary outcomes applied to unemployment data
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DOI：
10.1007/bf02925924
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发表时间：
2000-07-01
影响因子：
1.3
通讯作者：
Czado, C
中科院分区：
文献类型：
--
作者：
Czado, C
In panel studies binary outcome measures together with time stationary anti time varying explanatory variables are collected over time oil 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 all 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.
