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• W2526583983 abstract "My dissertation consists of three essays on spatialeconometrics. The first essay, “Model Selection Using J-test forthe Spatial Autoregressive Model vs. the Matrix Exponential SpatialModel”, studies the non-nested model selection problem between thespatial Autoregressive (SAR) model and the matrix exponentialspatial specification (MESS) model .We use the 2SLS and GMM methodsto implement the J-test procedure and derive several teststatistics under the GMM framework. We also extend the J-testprocedure to the setting when error terms in the model are withunknown heteroskedasticity. Monte Carlo results suggest with strongspatial dependence the J-test statistics have good power todistinguish the SAR and MESS models. In Essay Two, “BayesianEstimation and Model Selection for Spatial Durbin Error Model withFinite Distributed Lags”, we consider the Bayesian MCMC estimationof the model with a smoothness prior. The corresponding Bayesianmodel selection procedure for the SDEM model, the SAR model and theMESS model are studied and expressions of the marginal likelihoodof the three models are derived. Simulation results suggest thatthe Bayesian estimates of high order spatial distributed lagcoefficients are more precise than the maximum likelihoodestimates. When the data is generated with a general decliningpattern or a uni-modal pattern of spatial externalities, the SDEMmodel can better capture the pattern than the SAR and MESS modelsin most cases. We apply the estimation and model selectionprocedure to study the effect of right to work (RTW) laws onmanufacturing employment. My job market paper, “Bayesian Estimationof a Spatial Autoregressive Model with an Unobserved EndogenousSpatial Weight Matrix and Unobserved Factors”, examines thespecification and estimation of the SAR model with new features.Motivated by the spillover effects of state medicaid spending onwelfare programs, we combine all these new features together forthe first time in the SAR model. Specifically, we focus on two waysof defining neighborliness (a source of unobserved spatial weightmatrix W): one based on geographical distance and the other on“economic’’distance. In this particular application, endogeneity ofW comes from the correlation of economic distance and thedisturbances in the SAR equation. Unobserved factors are introducedto control for common shocks to all states. For the estimation ofthe model, the Bayesian MCMC method is employed, which is alsosupported by simulation results. We find that a dollar increase ina state’s neighbors’ Medicaid related spending will increase itsown Medicaid related spending by about 52 cents. Both geographicaland economic distances are shown to have significant effects on theinteraction strength of state Medicaid related spending. Ourresults suggest that in the context of Medicaid spending, welfaremotivated move and yardstick competition are both sources ofstrategic interactions among state governments." @default.
• W2526583983 created "2016-10-07" @default.
• W2526583983 creator A5049395133 @default.
• W2526583983 date "2014-01-01" @default.
• W2526583983 modified "2023-09-24" @default.
• W2526583983 title "Three Essays on Spatial Econometrics: Specification,Estimation and Model Selection for Spatial Models" @default.
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