A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity

Antonio Páez, Takashi Uchida and Kazuaki Miyamoto
Additional contact information
Antonio Páez: Center for Northeast Asian Studies, Tohoku University, Kawauchi, Aoba-ku, Sendai 980-8576, Japan
Takashi Uchida: Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 9558-8585, Japan
Kazuaki Miyamoto: Center for Northeast Asian Studies, Tohoku University, Kawauchi, Aoba-ku, Sendai 980-8576, Japan

Environment and Planning A, 2002, vol. 34, issue 4, 733-754

Abstract: Geographically weighted regression (GWR) has been proposed as a technique to explore spatial parametric nonstationarity. The method has been developed mainly along the lines of local regression and smoothing techniques, a strategy that has led to a number of difficult questions about the regularity conditions of the likelihood function, the effective number of degrees of freedom, and in general the relevance of extending the method to derive inference and model specification tests. In this paper we argue that placing GWR within a different statistical context, as a spatial model of error variance heterogeneity, or what might be termed locational heterogeneity, solves these difficulties. A maximum-likelihood-based framework for estimation and inference of a general geographically weighted regression model is presented that leads to a method to estimate location-specific kernel bandwidths. Moreover, a test for locational heterogeneity is derived and its use exemplified with a case study.

Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1068/a34110 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:34:y:2002:i:4:p:733-754

DOI: 10.1068/a34110

Access Statistics for this article

More articles in Environment and Planning A
Bibliographic data for series maintained by SAGE Publications ().