Handling spatial dependence under unknown unit locations. Issue 2 (3rd April 2021)

• Record Type: Journal Article
• Authors: Santi, Flavio; Dickson, Maria Michela; Espa, Giuseppe; Taufer, Emanuele; Mazzitelli, Andrea
• Is Part Of: Spatial economic analysis. Volume 16:Issue 2(2021)
• Page Start: 194
• Page End: 216
• Publication Date: 2021-04-03
• Subjects: spatial models -- spatial dependence -- regression -- approximate methods -- missing data; C21, C51, C52; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2020.1769171
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: When a regression analysis is performed on a variable that exhibits some form of spatial dependence, Gauss–Markov assumptions about the exogeneity of regressors and independence amongst error terms may be violated. If this is the case and no information is available about the location of units, point estimators and standard error estimators are typically heavily biased. The paper proposes an empirical solution that helps to mitigate spatial dependence in regression residuals when no information is available about unit positions. It illustrates the method, discusses some practical issues, provides some evidence through Monte Carlo simulations and includes an analysis of actual data from the real estate market.