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Bayesian analysis of spatial dynamic panel data model with convex combinations of different spatial weight matrices: A reparameterized approach

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  • Cai, Zhengzheng
  • Zhu, Yanli
  • Han, Xiaoyi

Abstract

This paper brings forward a novel Bayesian estimation and model selection method for spatial dynamic panel data model with convex combinations of different spatial weight matrices. We reparameterize the model as a high order spatial panel model, and recover the importance weights by a computationally tractable Markov Chain Monte Carlo (MCMC) sampler. We also propose a nested model selection procedure to test which spillover channel exerts significant influence and whether one channel dominates the others. Simulation suggests that the reparameterized approach works well both for parameter estimation and model selection.

Suggested Citation

  • Cai, Zhengzheng & Zhu, Yanli & Han, Xiaoyi, 2022. "Bayesian analysis of spatial dynamic panel data model with convex combinations of different spatial weight matrices: A reparameterized approach," Economics Letters, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:ecolet:v:217:y:2022:i:c:s0165176522002361
    DOI: 10.1016/j.econlet.2022.110695
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    References listed on IDEAS

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    More about this item

    Keywords

    Spatial dynamic panel data model; Convex combination; Reparameterized MCMC estimation; Model selection;
    All these keywords.

    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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