Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags

Xiaoyi Han and Lung-Fei Lee

Regional Science and Urban Economics, 2013, vol. 43, issue 5, 816-837

Abstract: In this paper we investigate a spatial Durbin error model with finite distributed lags and consider the Bayesian MCMC estimation of the model with a smoothness prior. We study also the corresponding Bayesian model selection procedure for the spatial Durbin error model, the spatial autoregressive model and the matrix exponential spatial specification model. We derive expressions of the marginal likelihood of the three models, which greatly simplify the model selection procedure. Simulation results suggest that the Bayesian estimates of high order spatial distributed lag coefficients are more precise than the maximum likelihood estimates. When the data is generated with a general declining pattern or a unimodal pattern for lag coefficients, the spatial Durbin error model can better capture the pattern than the SAR and the MESS models in most cases. We apply the procedure to study the effect of right to work (RTW) laws on manufacturing employment.

Keywords: Spatial Durbin error model; Spatial autoregressive model; Matrix exponential spatial specification; Smoothness prior; Marginal likelihood; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C11 C21 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:43:y:2013:i:5:p:816-837

DOI: 10.1016/j.regsciurbeco.2013.04.006

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