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.
Highlights
In panel data regression models, cross sectional and time effects are usually introduced to represent individual heterogeneity
If we use traditional panel data models to study the relationship between exchange rate and inflation based on data of different countries, this is equivalent to imposing the assumption that exchange rate has the same effect on inflation in all countries., which is somewhat far-fetched In order to overcome this drawback of traditional panel data models, economists propose random coefficients panel data models and varying coefficients panel data models in which the coefficients can vary with section units and times
In order to combine the advantages of spatial model and panel data smoothing transition regression model (PSTR) model, this paper introduces spatial correlation into PSTR model and proposes a spatial lag panel smooth transition regression (SLPSTR) model which can fully consider heterogeneity and spatial correlation simultaneously
Summary
In panel data regression models, cross sectional and time effects are usually introduced to represent individual heterogeneity. In the study of panel data spatial econometric model, [10] proposed the Bayesian inference for the spatial random effects model. The existing literatures recently focus on the linear spatial model with the assumption that the Bayesian panel smooth transition model with spatial correlation influence of the independent variables on the dependent variable is linear and the marginal effects are constant in different time and space.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
