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Activity Number: 513
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #307287
Title: Bayesian Seemingly Unrelated Regression in Spatial Regional Model: Economics of Agglomeration in Japan from 1991--2000
Author(s): Kazuhiko Kakamu*+ and Wolfgang Polasek and Hajime Wago
Companies: Institute of Advanced Studies and Institute for Advanced Studies and Nagoya University
Address: Stumpergasse 56, Vienna, 1060, Austria
Keywords: economics of agglomeration ; Markov chain Monte Carlo (MCMC) ; panel data ; seemingly unrelated regression (SUR) ; spatial autoregressive model (SAR) ; spatial error model (SEM)
Abstract:

This paper considers the seemingly unrelated regression (SUR) model with spatial dependency from a Bayesian point of view. We consider Markov chain Monte Carlo methods to estimate the parameters of the model. Our approach is illustrated with simulated and real data sets. In the real data example, we analyze the economics of agglomeration in Japan during the period 1991 to 2000.

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