Tomohiro Ando, Jushan Bai, Kunpeng Li, Yong Song
arXiv 2 Mar 2025 · Econometrics
arXiv:2503.00772 · PDF · DOI · OpenAlex · Extracted main text
With the rapid advancement of information technology and data collection systems, large-scale spatial panel data presents new methodological and computational challenges. This paper introduces a dynamic spatial panel quantile model that incorporates unobserved heterogeneity. The proposed model captures the dynamic structure of panel data, high-dimensional cross-sectional dependence, and allows for heterogeneous regression coefficients. To estimate the model, we propose a novel Bayesian Markov Chain Monte Carlo (MCMC) algorithm. Contributions to Bayesian computation include the development of quantile randomization, a new Gibbs sampler for structural parameters, and stabilization of the tail behavior of the inverse Gaussian random generator. We establish Bayesian consistency for the proposed estimation method as both the time and cross-sectional dimensions of the panel approach infinity. Monte Carlo simulations demonstrate the effectiveness of the method. Finally, we illustrate the applicability of the approach through a case study on the quantile co-movement structure of the gasoline market.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ando, T. and J. Bai (2020) Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity self | 0.874 | 6 | 2 | 100% |
| 2 | Chen, L., J. Gonzalo, and J. Dolado (2021) Quantile factor models | 0.737 | 3 | 2 | 100% |
| 3 | Yu, J., R. de Jong, and L. Lee (2008) Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both $n$ and $t$ are large | 0.737 | 3 | 2 | 100% |
| 4 | Beenstock, M. and D. Felsenstein (2015) Estimating spatial spillover in housing construction with nonstationary panel data | 0.405 | 1 | 1 | 100% |
| 5 | Ghosal, S., J. K. Ghosh, and R. V. Ramamoorthi (1999) Posterior consistency of dirichlet mixtures in density estimation | 0.405 | 1 | 1 | 100% |
| 6 | Glaser, S., R. Jung, and K. Schweikert (2022) Spatial panel count data: modeling and forecasting of urban crimes | 0.405 | 1 | 1 | 100% |
| 7 | Koenker, R. and G. Bassett (1978) Regression quantiles | 0.405 | 1 | 1 | 100% |
| 8 | Ando, T. and J. Bai (2017) Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures self | 0.405 | 1 | 1 | 100% |
| 9 | Anselin, L (1988) Spatial econometrics: methods and models | 0.405 | 1 | 1 | 100% |
| 10 | Aquaro, M., N. Bailey, and M. H. Pesaran (2021) Estimation and inference for spatial models with heterogeneous coefficients: an application to us house prices | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.