Spatial Econometrics

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

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