Heterogeneous regression models for clusters of spatial dependent data

Zhihua Ma, Yishu Xue and Guanyu Hu

Spatial Economic Analysis, 2020, vol. 15, issue 4, 459-475

Abstract: In economic development there are often regions that share similar socioeconomic characteristics, and econometrics models on such regions tend to produce similar covariate effect estimates. This paper proposes a Bayesian clustered regression for spatially dependent data in order to detect clusters in covariate effects. The proposed method is based on the Dirichlet process, which provides a probabilistic framework for simultaneous inference of the number of clusters and clustering configurations. The use of the method is illustrated both in simulation studies and by an application to a housing cost data set of Georgia.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/17421772.2020.1784989 (text/html)
Access to full text is restricted to subscribers.

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:taf:specan:v:15:y:2020:i:4:p:459-475

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RSEA20

DOI: 10.1080/17421772.2020.1784989

Access Statistics for this article

Spatial Economic Analysis is currently edited by Bernie Fingleton and Danilo Igliori

More articles in Spatial Economic Analysis from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().