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A Primer for Spatial EconometricsFurther Topics in Spatial Econometrics

A Primer for Spatial Econometrics: Further Topics in Spatial Econometrics [This chapter discusses some advanced special topics in spatial econometrics that have recently been introduced in the literature. The primary purpose is to make the reader knowledgeable on a set of techniques that represent the evolution of the methods presented in Chapter 3 and that constitute an essential part of the skill set currently required by spatial econometricians. These methods have the potential to make a tremendous impact in the analysis of real world problems in many scientific fields. In particular, section 4.1 discusses the case of non-constant innovation variances (heteroscedastic models), section 4.2 refers to the case where the dependent variable assumes a discrete (in particular, a binary) form, section 4.3 contains some of the modeling strategies in the field of diachronic spatial econometric models estimated on panel data and, finally, section 4.4 discusses regression models that are non-stationary in the geographical space. Following the introductory nature of the current presentation, we will discuss the various methods with less analytical detail compared to the rest of the book, while referring the interested reader to the current literature for more detail.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Primer for Spatial EconometricsFurther Topics in Spatial Econometrics

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References (60)

Publisher
Palgrave Macmillan UK
Copyright
© Palgrave Macmillan, a division of Macmillan Publishers Limited 2014
ISBN
978-1-137-42816-5
Pages
99 –166
DOI
10.1057/9781137317940_4
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter discusses some advanced special topics in spatial econometrics that have recently been introduced in the literature. The primary purpose is to make the reader knowledgeable on a set of techniques that represent the evolution of the methods presented in Chapter 3 and that constitute an essential part of the skill set currently required by spatial econometricians. These methods have the potential to make a tremendous impact in the analysis of real world problems in many scientific fields. In particular, section 4.1 discusses the case of non-constant innovation variances (heteroscedastic models), section 4.2 refers to the case where the dependent variable assumes a discrete (in particular, a binary) form, section 4.3 contains some of the modeling strategies in the field of diachronic spatial econometric models estimated on panel data and, finally, section 4.4 discusses regression models that are non-stationary in the geographical space. Following the introductory nature of the current presentation, we will discuss the various methods with less analytical detail compared to the rest of the book, while referring the interested reader to the current literature for more detail.]

Published: Nov 9, 2015

Keywords: Ordinary Little Square; Geographically Weight Regression; Spatial Error Model; Geographically Weight Regression Model; Spatial Econometric

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