Abstract

HETEROGENEOUS DYNAMIC MICRO PANEL MODEL: A HIERARCHICAL BAYESIAN APPROACH

Joseph Olutayo Iyaniwura1, AbosedeAdedayo Adepoju2* and Yemisi Omolara Akinlade3

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Abstract This paper uses a dynamic panel model to examine how unobserved individual heterogeneity affects parameters of inference. It is found that not accounting for the heterogeneity produces inconsistent estimates of the mean autoregressive coefficient, even for a panel with large N and T. Therefore, a great deal of interest was placed on hierarchical Bayesian estimation of unobserved individual heterogeneity of dynamic panel models, in order to improve on a static panel model. The method allows for unit-specific coefficients to be different across observations and imposing a stability condition for individual autoregressive coefficient drawn from a beta distribution (0, 1). The theoretical findings are accompanied by extensive Markov Chain Monte Carlo experiments. The examination of all the figures and tables indicate that the Bayesian method effectively handled the complicated pattern exhibited by the data as the dimension of N is large and T is small. Keyword: Unobserved individual heterogeneity, dynamic micro-panel data, hierarchical Bayesian approach, Markov Chain Monte Carlo Simulation

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