Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects
Methodology
2021-08-31 v2 Econometrics
Applications
Abstract
In regional economics research, a problem of interest is to detect similarities between regions, and estimate their shared coefficients in economics models. In this article, we propose a mixture of finite mixtures (MFM) clustered regression model with auxiliary covariates that account for similarities in demographic or economic characteristics over a spatial domain. Our Bayesian construction provides both inference for number of clusters and clustering configurations, and estimation for parameters for each cluster. Empirical performance of the proposed model is illustrated through simulation experiments, and further applied to a study of influential factors for monthly housing cost in Georgia.
Cite
@article{arxiv.2004.12022,
title = {Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects},
author = {Guanyu Hu and Yishu Xue and Zhihua Ma},
journal= {arXiv preprint arXiv:2004.12022},
year = {2021}
}