A Primer for Spatial Econometrics: With Applications in R, STATA and Python

by Giuseppe Arbia

Overview

This textbook offers a practical and engaging introduction to spatial econometric modelling, detailing the key models, methodologies and tools required to successfully apply a spatial approach.

The second edition contains new methodological developments, new references and new software routines in R that have emerged since the first edition published in 2014. It also extends practical applications with the use of the software STATA and of the programming language Python. The first software is used increasingly by many economists, applied econometricians and social scientists while the software Python is becoming the elective choice in many scientific applications. With new statistical appendices in R, STATA and Python, as well as worked examples, learning questions, exercises and technical definitions, this is a significantly expanded second edition that will be a valuable resource for advanced students of econometrics.

Product Details

ISBN-13:	9783031571824
Publisher:	Palgrave Macmillan
Publication date:	10/30/2024
Series:	Palgrave Texts in Econometrics
Sold by:	Barnes & Noble
Format:	eBook
File size:	8 MB

About the Author

Giuseppe Arbia is Full Professor of Economic Statistics at the Università Cattolica del Sacro Cuore of Rome, Italy, and Lecturer of Statistics at the Università della Svizzera Italiana in Lugano, Switzerland. He was formerly Visiting Professor at New York University, USA, and at many other international universities. He has published eight books and more than 100 articles on the topic of spatial statistics and econometrics and is currently the Chairman of the Spatial Econometrics Association and the Director of the Spatial Econometrics Advanced Institute.

Table of Contents

1. The classical linear regression model.- 2. Some important spatial definitions.- 3. Spatial linear regression models.- 4. Further topics in spatial econometrics.- 5. Alternative spatial model specifications in the era of Big Data.- 6. Conclusions: What’s next?.