关键词: 空间计量模型, 模型选择, MCMC, M-H抽样

Abstract: Spatial econometric model selection is an important part of the spatial econometric modeling，is also a key step in spatial econometric model empirical analysis. We have made a detailed theoretical analysis on Moran index test, LM test, likelihood function, three information criteria, Bayesian posterior probability, Markov chain Monte Carlo method of spatial econometric model selection analysis. On this basis, we make simulation analysis by using Matlab programming. The results show that, in the model clusters of extended spatial econometrics, Moran index and LM test based on OLS residuals are some limitations, the principle of maximum log likelihood values is lack of differentiation, LM test is effective to only distinguish between SEM and SAR model. The information criterion is effective on most models, but also appear wrong choice. When M-H algorithm given appropriate, MCMC method has higher test validity because of making full use of both the likelihood function and the prior information, and has completely accurate judgment in larger samples. Moreover, MCMC method is also very effective for different order spatial adjacency matrix of the spatial econometric model selection.

Key words: Spatial Econometric Model, Model Selection, MCMC, M-H Sampling