Bayesian Analysis

Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach

Samantha Leorato and Maura Mezzetti

Full-text: Open access

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.

Article information

Source
Bayesian Anal., Volume 11, Number 4 (2016), 1035-1069.

Dates
First available in Project Euclid: 29 October 2015

Permanent link to this document
https://projecteuclid.org/euclid.ba/1446124569

Digital Object Identifier
doi:10.1214/15-BA979

Mathematical Reviews number (MathSciNet)
MR3545473

Zentralblatt MATH identifier
1359.62189

Keywords
Bayesian inference Kronecker product separable covariance matrix inverse Wishart distribution spatial–temporal dependence

Citation

Leorato, Samantha; Mezzetti, Maura. Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach. Bayesian Anal. 11 (2016), no. 4, 1035--1069. doi:10.1214/15-BA979. https://projecteuclid.org/euclid.ba/1446124569


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