Spatial Econometrics: Qualitative and Limited Dependent Variables Vol: 37

Badi H. Baltagi
Syracuse University, USA

James P. LeSage
Texas State University - San Marcos, USA

R. Kelley Pace
Louisiana State University, USA

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Product Details
08 Dec 2016
Emerald Group Publishing Limited
408 pages - 152 x 229 x 31mm
Advances in Econometrics
Advances in Econometrics is a research annual whose editorial policy is to publish original research articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research. Volume 37 exemplifies this focus by highlighting key research from new developments in econometrics.
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
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
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. Distributed in North America by Turpin Distribution.

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