Prelims
Spatial Econometrics: Qualitative and Limited Dependent Variables
ISBN: 978-1-78560-986-2, eISBN: 978-1-78560-985-5
ISSN: 0731-9053
Publication date: 1 December 2016
Citation
(2016), "Prelims", Baltagi, B.H., Lesage, J.P. and Pace, R.K. (Ed.) Spatial Econometrics: Qualitative and Limited Dependent Variables (Advances in Econometrics, Vol. 37), Emerald Group Publishing Limited, Leeds, pp. i-x. https://doi.org/10.1108/S0731-905320160000037001
Publisher
:Emerald Group Publishing Limited
Copyright © 2017 Emerald Group Publishing Limited
Half Title Page
SPATIAL ECONOMETRICS: QUALITATIVE AND LIMITED DEPENDENT VARIABLES
Series Page
ADVANCES IN ECONOMETRICS
Series Editors: Thomas B. Fomby, R. Carter Hill, Ivan Jeliazkov, Juan Carlos Escanciano, Eric Hillebrand, Daniel L. Millimet, Rodney Strachan
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Title Page
ADVANCES IN ECONOMETRICS VOLUME 37
SPATIAL ECONOMETRICS: QUALITATIVE AND LIMITED DEPENDENT VARIABLES
EDITED BY
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
United Kingdom – North America – Japan India – Malaysia – China
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First edition 2017
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ISBN: 978-1-78560-986-2
ISSN: 0731-9053 (Series)
List of Contributors
| Badi H. Baltagi | Department of Economics and Center for Policy Research, Syracuse University, Syracuse, NY, USA |
| Peter Burridge | Department of Economics and Related Studies, University of York, York, UK |
| Raffaella Calabrese | Business School, University of Edinburgh, Edinburgh, UK |
| Mihaela Craioveanu | Department of Economics and Finance, University of Central Missouri, Warrensburg, MO, USA |
| Paul Damien | Department of Information Risk and Operations Management, The University of Texas at Austin, TX, USA |
| Jacob Dearmon | Meinders School of Business, Oklahoma City University, USA |
| Peter H. Egger | ETH Zurich, Zurich, Switzerland, CEPR, CESifo |
| J. Paul Elhorst | Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands |
| Johan A. Elkink | School of Politics and International Relations, University College Dublin, Dublin |
| Jaepil Han | Korea Development Institute, Sejong, South Korea |
| Michaela Kesina | ETH Zurich, Zurich, Switzerland |
| Kara M. Kockelman | Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, TX, USA |
| James P. LeSage | Department of Finance und Economics,Texas State University, San Marcos, TX, USA |
| Roman Liesenfeld | Institut für Ökonometrie und Statistik, Universität Köln, Cologne, Germany |
| R. Kelley Pace | Department of Finance, Louisiana State University, Baton Rouge, LA, USA |
| Jean-François Richard | Department of Economics, University of Pittsburgh, Pittsburgh, PA, USA |
| Deockhyun Ryu | School of Economics, Chung-Ang University, Seoul, South Korea |
| Robin Sickles | Department of Economics, Rice University, Houston, TX, USA |
| Yuxue Sheng | China-ASEAN Research Institute, Guangxi University, Nanning, China |
| Tony E. Smith | Department of Electrical and Systems Engineering, University of Pennsylvania, USA |
| Dek Terrell | Department of Economics, Louisiana State University, Baton Rouge, LA, USA |
| Jan Vogler | Institut für Ökonometrie und Statistik, Universität Köln, Cologne, Germany |
| Yiyi Wang | Civil Engineering Department, Montana State University, Bozeman, MT, USA |
| Xiaokun Wang | Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA |
| Katarina Zigova | Department of Economics, University of Konstanz, Konstanz, Germany |
| Wei Zou | Rensselaer Polytechnic Institute, Troy, NY, USA |
- Prelims
- 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
- Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables
- Part III: Discrete Dependent Variables – Bayesian
- The Impact of Storms on Firm Survival: A Bayesian Spatial Econometric Model for Firm Survival
- Bayesian Spatial Bivariate Panel Probit Estimation
- Estimating Binary Spatial Autoregressive Models for Rare Events
- A Multivariate Spatial Analysis for Anticipating New Firm Counts
- A Multivariate Spatial-Time of Day Analysis of Truck Crash Frequency across Neighborhoods in New York City
- Part IV: Continuous Dependent Variables – Maximum Likelihood
- Group Interaction in Research and the Use of General Nesting Spatial Models
- How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model
- Part V: Continuous Dependent Variables – Bayesian
- Local Marginal Analysis of Spatial Data: A Gaussian Process Regression Approach with Bayesian Model and Kernel Averaging
- City and Industry Network Impacts on Innovation by Chinese Manufacturing Firms: A Hierarchical Spatial-Interindustry Model