
Spatial Regression Models
SAGE Publications Inc (Publisher)
1st Edition
Published on 23. April 2008
Book
Paperback/Softback
112 pages
978-1-4129-5415-0 (ISBN)
Article exhausted; check for reprint
Description
Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.
More details
Series
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 216 mm
Width: 140 mm
Weight
142 gr
ISBN-13
978-1-4129-5415-0 (9781412954150)
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
New editions

Michael D. Ward | Kristian Skrede Gleditsch
Spatial Regression Models
Book
07/2018
2nd Edition
SAGE Publications Inc
€65.79
Shipment within 15-20 days
Persons
Michael D. Ward is Professor of Political Science at Duke University. He is an affiliate of the Duke Network Analysis Center. His primary interests are in international relations (spanning democratization, globalization, international commerce, military spending, as well as international conflict and cooperation), political geography, as well as mathematical and statistical methods. Kristian Skrede Gleditsch is Professor in the Department of Government, University of Essex and a Research Associate at the Centre for the Study of Civil War, PRIO. His research interests include conflict and cooperation, democratization, and spatial dimensions of social and political processes. He is the author of All International Politics is Local: The Diffusion of Conflict, Integration, and Democratization (University of Michigan Press, 2002) and Spatial Regression Models (Sage, 2008, with Michael D. Ward) as well as articles in journals including American Journal of Political Science, American Political Science Review, Annals of the Association of American Geographers, Biological Reviews, International Interactions, International Organization, International Studies Quarterly, Journal of Conflict Resolution, Journal of Peace Research, Political Analysis, Political Psychology, and World Politics.
Content
Preface
Chapter 1: Introduction
Interaction and Social Science
Democracy Around the World
Introducing Spatial Dependence
Maps as Visual Displays of Data
Measuring Spatial Association and Correlation
Measuring Proximity
Estimating Spatial Models
Summary
Chapter 2: Spatially Lagged Dependent Variables
Regression with Spatially Lagged Dependent Variables
Estimating the Spatially Lagged y Model
Maximum Likelihood Estimates of the Spatially Lagged Y Model of Democracy
Equilibrium Effects in the Spatially Lagged y Model
Spatial Dependence in Turnout in Italy
Using Different Weights Matrices in a Spatially Lagged Dependent Variable Model
The Spatially Lagged Dependent Variable Versus OLS with Dummy Variables
Summary
Chapter 3: Spatial Error Model
The Spatial Error Model
Maximum Likelihood Estimation of the Spatial Errors Model
Example: Democracy and Development
Spatially Lagged y Versus Spatial Errors
Assessing Spatial Error in Dyadic Trade Flows
Summary
Chapter 4: Extensions
Specifying Connectivities
Inference and Model Evaluation
Summary
Appendix: Software Options
References
Chapter 1: Introduction
Interaction and Social Science
Democracy Around the World
Introducing Spatial Dependence
Maps as Visual Displays of Data
Measuring Spatial Association and Correlation
Measuring Proximity
Estimating Spatial Models
Summary
Chapter 2: Spatially Lagged Dependent Variables
Regression with Spatially Lagged Dependent Variables
Estimating the Spatially Lagged y Model
Maximum Likelihood Estimates of the Spatially Lagged Y Model of Democracy
Equilibrium Effects in the Spatially Lagged y Model
Spatial Dependence in Turnout in Italy
Using Different Weights Matrices in a Spatially Lagged Dependent Variable Model
The Spatially Lagged Dependent Variable Versus OLS with Dummy Variables
Summary
Chapter 3: Spatial Error Model
The Spatial Error Model
Maximum Likelihood Estimation of the Spatial Errors Model
Example: Democracy and Development
Spatially Lagged y Versus Spatial Errors
Assessing Spatial Error in Dyadic Trade Flows
Summary
Chapter 4: Extensions
Specifying Connectivities
Inference and Model Evaluation
Summary
Appendix: Software Options
References