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- W2920204233 abstract "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." @default.
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- W2920204233 date "2019-03-04" @default.
- W2920204233 modified "2023-10-17" @default.
- W2920204233 title "Bayesian panel smooth transition model with spatial correlation" @default.
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- W2920204233 doi "https://doi.org/10.1371/journal.pone.0211467" @default.
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