Model Selection in Spatial Autoregressive Models with Varying Coefficients

Hongjie Wei, Yan Sun () and Meidi Hu
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Hongjie Wei: School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China
Yan Sun: School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China
Meidi Hu: School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China

Frontiers of Economics in China-Selected Publications from Chinese Universities, 2018, vol. 13, issue 4, 559-576

Abstract: Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.

Keywords: heterogeneous effects; varying coefficient; spatial dependence; model selection; adaptive group LASSO (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
Date: 2018
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