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Bayesian group shrinkage for spatial autoregressive model with convex combination of spatial weights matrices

Author

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  • Cai, Zhengzheng
  • Han, Xiaoyi
  • Zhuo, Jianchao

Abstract

This paper investigates the spatial autoregressive model with convex combination of spatial weights matrices categorized in groups, an empirically relevant specification that has received limited attention in the spatial econometrics literature. We employ the Group Inverse-Gamma Gamma (GIGG) prior introduced by Boss et al. (2024) to distinguish between relevant and irrelevant groups, identify influential spatial weights matrices, and mitigate multicollinearity arising from correlations among matrices within the same group. A computationally efficient Markov Chain Monte Carlo (MCMC) algorithm is developed for estimation, inference, and hyperparameter tuning. Simulation studies demonstrate that the proposed prior performs effectively in both parameter estimation and variable selection.

Suggested Citation

  • Cai, Zhengzheng & Han, Xiaoyi & Zhuo, Jianchao, 2026. "Bayesian group shrinkage for spatial autoregressive model with convex combination of spatial weights matrices," Economics Letters, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:ecolet:v:267:y:2026:i:c:s0165176526003009
    DOI: 10.1016/j.econlet.2026.113104
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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