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December 2016 Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach
Samantha Leorato, Maura Mezzetti
Bayesian Anal. 11(4): 1035-1069 (December 2016). DOI: 10.1214/15-BA979

Abstract

A hierarchical Bayesian model for spatial panel data is proposed. The idea behind the proposed method is to analyze spatially dependent panel data by means of a separable covariance matrix. Let us indicate the observations as yit, in i=1,,N regions and at t=1,,T times, and suppose the covariance matrix of y, given a set of regressors, is written as a Kronecker product of a purely spatial and a purely temporal covariance. On the one hand, the structure of separable covariances dramatically reduces the number of parameters, while on the other hand, the lack of a structured pattern for spatial and temporal covariances permits capturing possible unknown dependencies (both in time and space). The use of the Bayesian approach allows one to overcome some of the difficulties of the classical (MLE or GMM based) approach. We present two illustrative examples: the estimation of cigarette price elasticity and of the determinants of the house price in 120 municipalities in the Province of Rome.

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Samantha Leorato. Maura Mezzetti. "Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach." Bayesian Anal. 11 (4) 1035 - 1069, December 2016. https://doi.org/10.1214/15-BA979

Information

Published: December 2016
First available in Project Euclid: 29 October 2015

zbMATH: 1359.62189
MathSciNet: MR3545473
Digital Object Identifier: 10.1214/15-BA979

Rights: Copyright © 2016 International Society for Bayesian Analysis

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Vol.11 • No. 4 • December 2016
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