A Primer For Spatial Econometrics


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A Primer for Spatial Econometrics


A Primer for Spatial Econometrics

Author: Giuseppe Arbia

language: en

Publisher: Springer Nature

Release Date: 2024-10-30


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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.

A Primer for Spatial Econometrics


A Primer for Spatial Econometrics

Author: G. Arbia

language: en

Publisher: Springer

Release Date: 2014-06-30


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This book aims at meeting the growing demand in the field by introducing the basic spatial econometrics methodologies to a wide variety of researchers. It provides a practical guide that illustrates the potential of spatial econometric modelling, discusses problems and solutions and interprets empirical results.

Introduction to Spatial Econometrics


Introduction to Spatial Econometrics

Author: James LeSage

language: en

Publisher: CRC Press

Release Date: 2009-01-20


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Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat