关键词: 时变系数广义空间滞后模型, 贝叶斯估计, MCMC方法

Abstract: Traditional spatial econometric models use a single invariant coefficient to characterize the spatial relationship between the units. However, with the increase of individuals and time in the sample, the invariant coefficient model cannot accurately reflect the time variability of the spatial relationship, which may lead to biased parameter estimates. Based on this, this paper constructs a generalized spatial lag model with time-varying coefficients, uses Bayesian method and MCMC sampling to estimate model parameters, compares it with invariant coefficient model, and finally applies it to specific examples. The numerical simulation results show that the mean deviation and root mean square error of the parameter estimates of the time-varying coefficient model are smaller than those of the invariant coefficient model, and the root mean square error decreases with the increase of individuals or time. The example application not only re-measures the timevarying effect of the impact of industrial agglomeration on the upgrading of industrial structure, but also confirms the practicability of the model and method.

Key words: Generalized Spatial Lag Models with Time-varying Coefficients, Bayesian Estimation;MCMC Method