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Inference in Panel Data Models via Gibbs Sampling

  • Siddhartha Chib
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 33)

Abstract

In this chapter we consider the use of the recently developed Gibbs sampling method to estimate panel data models. The Gibbs sampler is a Markov chain Monte-Carlo (MCMC) method that provides an approach to simulating a given joint distribution. Although this method can be employed quite generally it has proved most useful in Bayesian inference where it has been used to simulate posterior distributions in a number of different settings (Geman and Geman [1984], Gelfand and Smith [1990], Tierney [1994], and Chib and Greenberg [1993]). Once a sample of parameter draws from the posterior distribution has been obtained it is possible to estimate a parameter of interest by taking empirical averages of the simulated values.

Keywords

Posterior Distribution Gibbs Sampling American Statistical Association Markov Chain Monte Carlo Method Panel Data Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Siddhartha Chib

There are no affiliations available

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