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A Bayesian inference for fixed effect panel probit model
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 Title & Authors
A Bayesian inference for fixed effect panel probit model
Lee, Seung-Chun;
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 Abstract
The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.
 Keywords
panel probit model;Gibbs sampling;incidental parameters problem;fixed effects;random effects;individual-specific effects;
 Language
English
 Cited by
 References
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