Machine generated contents note: 1.The Classical Linear Regression Model -- 1.1.The basic linear regression model -- 1.2.Non-sphericity of the disturbances -- 1.3.Endogeneity -- 1.4.R Codes: running a regression -- Key Terms and Concepts Introduced -- Questions -- Exercises -- References to the Chapter -- 2.Some Important Spatial Definitions -- 2.1.The Spatial Weight Matrix W and the definition of Spatial Lag -- 2.2.Testing spatial autocorrelation among OLS residuals without an explicit alternative hypothesis -- 2.3.R Codes -- Key Terms and Concepts Introduced -- Questions -- Exercises -- References to the Chapter -- 3.Spatial Linear Regression Models -- 3.1.Generalities -- 3.2.Pure spatial autoregression -- 3.3.The classical model with spatially lagged non-stochastic regressors -- 3.4.The Spatial Error Model (SEM) -- 3.5.The Spatial Lag Model (SLM) -- 3.6.The general SARAR(1,1) Model Note continued: 3.7.Testing spatial autocorrelation among the residuals with an explicit alternative hypothesis -- 3.8.Interpretation of the parameters in spatial econometric models -- 3.9.R Codes: estimating linear spatial models -- Key Terms and Concepts Introduced -- Questions -- Exercises -- References to the Chapter -- 4.Further Topics in Spatial Econometrics -- 4.1.Heteroscedastic innovations -- 4.2.Spatial models for binary response variables -- 4.3.Spatial panel data models (Written by Giovanni Millo) -- 4.4.Non-stationary spatial econometric models -- 4.5.R Codes -- Key Terms and Concepts Introduced -- Questions -- Exercises -- References to the Chapter -- 5.Alternative Model Specifications for Big Datasets -- 5.1.Introduction -- 5.2.The MESS specification -- 5.3.The unilateral approximation approach -- 5.4.A composite likelihood approach -- 5.5.R Codes -- Key Terms and Concepts Introduced -- Questions -- Exercises -- References to the Chapter
Summary:
A Primer for Spatial Econometrics aims to meet a growing demand in the field by introducing basic spatial econometrics methodologies to a wide variety of researchers. Spatial econometrics is a relatively new topic that is becoming increasingly popular in many of the social sciences. Readers will find this text to be an approachable, informative springboard for their own research and an invaluable support for those that want to start working immediately with the methods. It moves beyond previous studies as it is aimed explicitly at bridging the gap between a basic econometric textbook and more specialized texts in the subject. This book provides a practical guide that illustrates the potential of spatial econometric modelling, discusses problems and solutions and enables the reader both to interpret empirical results correctly and to start working with the methods. It provides essential notions and key insights as well as providing references for further reading to more in-depth discussions. Readers will appreciate the extensive presentation of examples in 'R', which has emerged as the software of choice for model builders in this area.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.