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.
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Chib, S. (1996). Inference in Panel Data Models via Gibbs Sampling. In: Mátyás, L., Sevestre, P. (eds) The Econometrics of Panel Data. Advanced Studies in Theoretical and Applied Econometrics, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0137-7_24
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DOI: https://doi.org/10.1007/978-94-009-0137-7_24
Publisher Name: Springer, Dordrecht
Print ISBN: 978-0-7923-3787-4
Online ISBN: 978-94-009-0137-7
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