Spatial Data Analysis and Econometrics

Michael Beenstock*, Daniel Felsenstein

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

Key developments in the econometric analysis of spatial cross-section data are reviewed. The spatial connectivity matrix (W) is introduced and its implications for spatial autocorrelation (SAC) is explained. Alternative statistical tests for spatial autocorrelation are reviewed. The spatial autoregression model (SAR) is introduced and its relation to regression models with spatial lagged dependent variables is explained. A common factor test is described, which tests the hypothesis that SAC is induced by the omission of spatial lagged dependent variables. Alternative estimation methods for spatial lag models are compared and contrasted, including maximum likelihood and instrumental variable methods. Spatial statistical methods such as spatial principal components generated by W, spatial filtering and geographically weighted regression are reviewed. The fundamental differences between spatial data and time series data are emphasized. Time is inherently sequential whereas space is not. Time is potentially infinite whereas space is not. Time has a natural unit of measurement (hours, months, years) whereas space does not. The MAUP (modifiable area unit problem) is discussed, which arises because, unlike physical space, socioeconomic space does not have a natural unit of measurement.

Original languageAmerican English
Title of host publicationAdvances in Spatial Science
PublisherSpringer International Publishing
Pages49-69
Number of pages21
DOIs
StatePublished - 2019

Publication series

NameAdvances in Spatial Science
ISSN (Print)1430-9602
ISSN (Electronic)2197-9375

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Fingerprint

Dive into the research topics of 'Spatial Data Analysis and Econometrics'. Together they form a unique fingerprint.

Cite this