Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags
AbstractIn 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Regional Science and Urban Economics.
Volume (Year): 43 (2013)
Issue (Month): 5 ()
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Web page: http://www.elsevier.com/locate/regec
Spatial Durbin error model; Spatial autoregressive model; Matrix exponential spatial specification; Smoothness prior; Marginal likelihood; Bayesian estimation;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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