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October 13, 2025Open Access

Bayesian inference for dynamic spatial quantile models with interactive effects

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Authors

TATomohiro AndoHiroshima UniversityJBJushan BaiColumbia UniversityKLKunpeng LiHohai University

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Implication

This research reports a dynamic spatial model for panel data, revealing heterogeneous coefficients, implying new computational strategies.

Key Points

  • The proposed model effectively captures dynamic structures and heterogeneous regression coefficients in spatial panel data.
  • Using Markov Chain Monte Carlo, the method processes large-scale data, enhancing Bayesian computation techniques significantly.
  • Monte Carlo simulations confirm the model's accuracy, demonstrating its advantages over traditional approaches for estimating spatial dependencies.
  • The case study on the gasoline market illustrates practical applications, enhancing understanding of quantile co-movement structures.

Cite This Study

Ando et al. (2025) studied this question.

synapsesocial.com/papers/68ecc715d1cc7436f7d18b85https://doi.org/10.48550/arxiv.2503.00772
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