DO SPATIAL EFFECFS REALLY MATTER IN REGRESSION ANALYSIS?

Luc Anselin · Daniel A. Griffith

568 citations0/0 files storedPapers of the Regional Science Association

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Spatial and Panel Data Analysis Regional Economics and Spatial Analysis Regional Economic and Spatial Analysis

Abstract

ABSTRACT A substantial hotly of applied statistical and econometric analysis in regional science and geography deals with data collected for aggregate spatial units of observation. These data are typically affected by a variety of measurement problems, resulting in spatial dependence and spatial heterogeneity. However, most of the empirical work fails to take this into account. In this paper, we address the issue of the extent to which spatial effects matter in applied regression analysis. An overview of the formal methodological problems is given, and related to the literature in spatial statistics and spatial econometrics.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 568 citations exist upstream.

Cited by

1995Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results 2022Influence of digital finance and green technology innovation on China's carbon emission efficiency: Empirical analysis based on spatial metrology 2003Spatial autocorrelation in multi-scale land use models 2000Statistical Analysis with ArcView GIS 2007Reflections on spatial autocorrelation 1992Spatial statistical analysis and geographic information systems 2005Environmental Kuznets curves: A spatial econometric approach 1997Bayesian Estimation of Spatial Autoregressive Models

Imported from OpenAlex (CC0) — nothing here is generated.