Bayesian estimation and application of semiparametric spatial lag quantile regression model
This paper proposes a semi-parametric spatial lag quantile regression model,which can simultaneously examine the spatial correlation of the dependent variables and the partial nonlinearity of the influencing mechanism,and can model the response function at any quantile.Secondly,the paper constructs a Bayesian estimation method for the model.In the construction of the Bayesian theoretical framework,the paper uses polynomial splines to fit the unknown non-parametric functions,and samples all parameters by combining reversible jump markov chain monte carlo(RJMCMC)algorithm,random walk Metropolis sampler and Gibbs sampling tech-nique.Then the accuracy of parameter estimation,the fitting effect of unknown function and the effect of practical application are investigated by numerical simulation method and application example.The results show that the accuracy of parameter estimates at three different quantile is higher under two different spatial data structures and a variety of different sample sizes.And the fitting effect of non-parametric unknown functions is good.The practical application of the theoretical method is also demonstrated.The results of this paper demonstrate that the proposed model and its theoretical approach can provide a powerful analytical tool for variables and data with both linear and nonlinear relationships,and with thick tails and spatial dependencies.
semi-parameterspatial lag modelquantile regressionBayesian estimation
万方数据
维普
