Heterogeneous regression models for clusters of spatial dependent data

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.

KEYWORDS:

• clustered coefficients regression
• Dirichlet process
• Markov chain Monte Carlo (MCMC)
• spatial random effects

JEL:

• C31
• C11
• C5

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Funding

Zhihua Ma’s research was supported by the Project of the Educational Commission of Guangdong Province of China [grant number #2019WQNCX104]. The data that support the findings of this study are openly available at www.healthanalytics.gatech.edu.