Spatial Econometrics
Qualitative and Limited Dependent Variables
Published on 8. December 2016
Book
Hardback
408 pages
978-1-78560-986-2 (ISBN)
Description
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.
Reviews / Votes
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. -- Annotation (c)2017 * (protoview.com) *More details
Series
Language
English
Place of publication
Bingley
United Kingdom
Publishing group
Emerald Publishing Limited
Target group
Professional and scholarly
College/higher education
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 26 mm
Weight
739 gr
ISBN-13
978-1-78560-986-2 (9781785609862)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Badi H. Baltagi | James P. LeSage | R. Kelley Pace
Spatial Econometrics
Qualitative and Limited Dependent Variables
E-Book
12/2016
Emerald Publishing Limited
€134.99
Available for download
Persons
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
James P. Lesage, Texas State University, San Marcos, TX, USA
R. Kelley Pace, Louisiana State University, Baton Rouge, LA, USA
Content
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-Francois 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
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-Francois 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