Bayesian Methods for Economic Panel Data

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In this research, I considered the Bayesian estimation methods for economic panel data using Markov chain Monte Carlo (MCMC). First, I proposed a Bayesian method for estimating the almost ideal demand system proposed by Deaton and Muellbauer using the panel data that includes zero expenditures. I dealt explicitly with the problem of zero expenditures in the model and estimate the almost ideal demand system that satisfy the adding-up condition. Furthermore, using MCMC, I estimated unobservable parameters including consumption of commodities, total consumption and equivalence scale and used their posterior distributions to calculate inequality measures, total consumption elasticities, and price elasticities.Next, I proposed the Bayesian method for the static pseudo panel model which is based on Hausman and Taylor model. Further, I extended the static pseudo panel model to the dynamic pseudo panel model and proposed the Bayesian estimation method. It is noteworthy that in the Bayesian approach no additional variation data (i.e., instrumental variables) are required in contrast with non-Bayesian approach.I wrote three articles on this project. One of them has already submitted to an international journal. I make the others ready to submit.