Bayesian Techniques in Spatial and Network Econometrics: 2. Computational Methods and Algorithms

Leslie W. Hepple

59 citations0/0 files storedEnvironment and Planning A Economy and Space

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Spatial and Panel Data Analysis Statistical Methods and Bayesian Inference Markov Chains and Monte Carlo Methods

Abstract

Bayesian theory has been seen as having considerable potential and attractiveness for model estimation and analysis in spatial and network econometrics. However, analytical and computational problems have also been seen as a great barrier. In this paper the analytical simplifications available are developed and the algorithms required are examined. The author argues that, for a broad class of models in spatial econometrics, Bayesian analysis is quite practicable and can be implemented without great cost. The spatial specifications are mapped into the various forms of Bayesian computation available and detailed examples are provided. Recent developments on the frontier of Bayesian computation have potential to expand further the practical applicability of the Bayesian approach to spatial econometrics.

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References

1988Spatial Econometrics: Methods and Models 1974Spatial Interaction and the Statistical Analysis of Lattice Systems 1981Spatial Statistics 1975Estimation Methods for Models of Spatial Interaction 1993Budget spillovers and fiscal policy interdependence 1984Maximum Likelihood Estimation of Models for Residual Covariance in Spatial Regression 1990Spatial Data Analysis in the Social and Environmental Sciences 1991Spatial Patterns in Household Demand

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