Badi H. Baltagi, Syracuse University, Syracuse, NY, USAJames P. Lesage, Texas State University, San Marcos, TX, USAR. Kelley Pace, Louisiana State University, Baton Rouge, LA, USA

Seven of the eleven papers in this collection explain how to estimate discrete dependent variables with spatial dependence using maximum likelihood and how to estimate binary and count dependent variables using Bayesian methods. A generic algorithm for numerically accurate likelihood evaluates spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The remaining four papers address continuous dependent variables for modeling group interaction in research, the spillover effects of public capital stock, government and industry impacts on innovation, and Boston housing data.

• PART I: INTRODUCTIONProgress In Spatial Modeling Of Discrete And Continuous Dependent Variables PART II: DISCRETE DEPENDENT VARIABLES MAXIMUM LIKELIHOODFast Simulated Maximum Likelihood Estimation Of The Spatial Probit Model Capable Of Handling Large Samples - R. Kelley Pace and James P. LeSageLikelihood Evaluation Of High-Dimensional Spatial Latent Gaussian Models With Non-Gaussian Response Variables - Roman Liesenfeld, Jean-François Richard and Jan VoglerPART III: DISCRETE DEPENDENT VARIABLES BAYESIANThe Impact Of Storms On Firm Survival: A Bayesian Spatial Econometric Model For Firm Survival - Mihaela Craioveanu and Dek TerrellvBayesian Spatial Bivariate Panel Probit Estimation - Badi H. Baltagi, Peter H. Egger and Michaela KesinaEstimating Binary Spatial Autoregressive Models For Rare Events - Raffaella Calabrese and Johan A. ElkinkA Multivariate Spatial Analysis For Anticipating New Firm Counts - Yiyi Wang, Kara M. Kockelman and Paul DamienA Multivariate Spatial-Time Of Day Analysis Of Truck Crash Frequency Across Neighborhoods In New York City - Wei Zou, Xiaokun Wang and Yiyi WangPART IV: CONTINUOUS DEPENDENT VARIABLES MAXIMUM LIKELIHOODGroup Interaction In Research And The Use Of General Nesting Spatial Models - Peter Burridge, J. Paul Elhorst and Katarina ZigovaHow To Measure Spillover Effects Of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model - Jaepil Han, Deockhyun Ryu and Robin SicklesPART V: CONTINUOUS DEPENDENT VARIABLES BAYESIANLocal Marginal Analysis Of Spatial Data: A Gaussian Process Regression Approach With Bayesian Model And Kernel Averaging - Jacob Dearmon and Tony E. SmithCity And Industry Network Impacts On Innovation By Chinese Manufacturing Firms: A Hierarchical Spatial- Interindustry Model - Yuxue Sheng and James P. LeSage

Badi H. Baltagi, Syracuse University, Syracuse, NY, USAJames P. Lesage, Texas State University, San Marcos, TX, USAR. Kelley Pace, Louisiana State University, Baton Rouge, LA, USA

Seven of the eleven papers in this collection explain how to estimate discrete dependent variables with spatial dependence using maximum likelihood and how to estimate binary and count dependent variables using Bayesian methods. A generic algorithm for numerically accurate likelihood evaluates spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The remaining four papers address continuous dependent variables for modeling group interaction in research, the spillover effects of public capital stock, government and industry impacts on innovation, and Boston housing data.

• PART I: INTRODUCTIONProgress In Spatial Modeling Of Discrete And Continuous Dependent Variables PART II: DISCRETE DEPENDENT VARIABLES MAXIMUM LIKELIHOODFast Simulated Maximum Likelihood Estimation Of The Spatial Probit Model Capable Of Handling Large Samples - R. Kelley Pace and James P. LeSageLikelihood Evaluation Of High-Dimensional Spatial Latent Gaussian Models With Non-Gaussian Response Variables - Roman Liesenfeld, Jean-François Richard and Jan VoglerPART III: DISCRETE DEPENDENT VARIABLES BAYESIANThe Impact Of Storms On Firm Survival: A Bayesian Spatial Econometric Model For Firm Survival - Mihaela Craioveanu and Dek TerrellvBayesian Spatial Bivariate Panel Probit Estimation - Badi H. Baltagi, Peter H. Egger and Michaela KesinaEstimating Binary Spatial Autoregressive Models For Rare Events - Raffaella Calabrese and Johan A. ElkinkA Multivariate Spatial Analysis For Anticipating New Firm Counts - Yiyi Wang, Kara M. Kockelman and Paul DamienA Multivariate Spatial-Time Of Day Analysis Of Truck Crash Frequency Across Neighborhoods In New York City - Wei Zou, Xiaokun Wang and Yiyi WangPART IV: CONTINUOUS DEPENDENT VARIABLES MAXIMUM LIKELIHOODGroup Interaction In Research And The Use Of General Nesting Spatial Models - Peter Burridge, J. Paul Elhorst and Katarina ZigovaHow To Measure Spillover Effects Of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model - Jaepil Han, Deockhyun Ryu and Robin SicklesPART V: CONTINUOUS DEPENDENT VARIABLES BAYESIANLocal Marginal Analysis Of Spatial Data: A Gaussian Process Regression Approach With Bayesian Model And Kernel Averaging - Jacob Dearmon and Tony E. SmithCity And Industry Network Impacts On Innovation By Chinese Manufacturing Firms: A Hierarchical Spatial- Interindustry Model - Yuxue Sheng and James P. LeSage

Badi H. Baltagi, Syracuse University, Syracuse, NY, USAJames P. Lesage, Texas State University, San Marcos, TX, USAR. Kelley Pace, Louisiana State University, Baton Rouge, LA, USA

Seven of the eleven papers in this collection explain how to estimate discrete dependent variables with spatial dependence using maximum likelihood and how to estimate binary and count dependent variables using Bayesian methods. A generic algorithm for numerically accurate likelihood evaluates spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The remaining four papers address continuous dependent variables for modeling group interaction in research, the spillover effects of public capital stock, government and industry impacts on innovation, and Boston housing data.

• PART I: INTRODUCTIONProgress In Spatial Modeling Of Discrete And Continuous Dependent Variables PART II: DISCRETE DEPENDENT VARIABLES MAXIMUM LIKELIHOODFast Simulated Maximum Likelihood Estimation Of The Spatial Probit Model Capable Of Handling Large Samples - R. Kelley Pace and James P. LeSageLikelihood Evaluation Of High-Dimensional Spatial Latent Gaussian Models With Non-Gaussian Response Variables - Roman Liesenfeld, Jean-François Richard and Jan VoglerPART III: DISCRETE DEPENDENT VARIABLES BAYESIANThe Impact Of Storms On Firm Survival: A Bayesian Spatial Econometric Model For Firm Survival - Mihaela Craioveanu and Dek TerrellvBayesian Spatial Bivariate Panel Probit Estimation - Badi H. Baltagi, Peter H. Egger and Michaela KesinaEstimating Binary Spatial Autoregressive Models For Rare Events - Raffaella Calabrese and Johan A. ElkinkA Multivariate Spatial Analysis For Anticipating New Firm Counts - Yiyi Wang, Kara M. Kockelman and Paul DamienA Multivariate Spatial-Time Of Day Analysis Of Truck Crash Frequency Across Neighborhoods In New York City - Wei Zou, Xiaokun Wang and Yiyi WangPART IV: CONTINUOUS DEPENDENT VARIABLES MAXIMUM LIKELIHOODGroup Interaction In Research And The Use Of General Nesting Spatial Models - Peter Burridge, J. Paul Elhorst and Katarina ZigovaHow To Measure Spillover Effects Of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model - Jaepil Han, Deockhyun Ryu and Robin SicklesPART V: CONTINUOUS DEPENDENT VARIABLES BAYESIANLocal Marginal Analysis Of Spatial Data: A Gaussian Process Regression Approach With Bayesian Model And Kernel Averaging - Jacob Dearmon and Tony E. SmithCity And Industry Network Impacts On Innovation By Chinese Manufacturing Firms: A Hierarchical Spatial- Interindustry Model - Yuxue Sheng and James P. LeSage