Bayesian panel smooth transition model with spatial correlation

Kunming Li, Liting Fang, Tao Lu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, we propose a spatial lag panel smoothing transition regression (SLPSTR) model ty considering spatial correlation of dependent variable in panel smooth transition regression model. This model combines advantages of both smooth transition model and spatial econometric model and can be used to deal with panel data with wide range of heterogeneity and cross-section correlation simultaneously. We also propose a Bayesian estimation approach in which the Metropolis-Hastings algorithm and the method of Gibbs are used for sampling design for SLPSTR model. A simulation study and a real data study are conducted to investigate the performance of the proposed model and the Bayesian estimation approach in practice. The results indicate that our theoretical method is applicable to spatial data with a wide range of spatial structures under finite sample.

Original languageEnglish
Article numbere0211467
JournalPloS one
Issue number3
StatePublished - Mar 2019


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