Dimitrakopoulos, S orcid.org/0000-0002-0043-180X (2019) Bayesian estimation of panel count data models: dynamics, latent heterogeneity, serial error correlation and nonparametric structures. In: Panel Data Econometrics. Academic Press , pp. 147-173. ISBN 978-0-12-814367-4
Abstract
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with exponential conditional mean, a well-known panel count data model. In particular, we focus on the implementation of Markov Chain Monte Carlo methods to various specifications of this model that allow for dynamics, latent heterogeneity and/or serial error correlation. The latent heterogeneity distribution is assigned a nonparametric structure, which is based on the Dirichlet process prior. The initial conditions problem also is addressed. For each resulting model specification, we provide the associated inferential algorithm for conducting posterior simulation. Finally, relevant computer codes are posted as an online supplement.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Keywords: | Count panel data; Dirichlet process; Dynamics; Latent heterogeneity; Markov Chain Monte Carlo; Serial error correlation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jul 2019 15:00 |
Last Modified: | 17 Jul 2019 15:00 |
Status: | Published |
Publisher: | Academic Press |
Identification Number: | 10.1016/B978-0-12-814367-4.00006-X |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147173 |