The Theoretical And Applied Research Of Spatial Econometric Model

Spatial econometrics is the study of the theory of impact of space geographic distribution on economic phenomena, from space theory was introduced, spatial econometric theory has been rapidly developed, and quickly formed an important modern branch of econometrics, obtained a number of important theoretical and practical results. Through the spatial weighting matrix, the impact of space factors on the economic phenomena is introduced into the model, some estimation and testing problems yield because of this, from different point of view, scholars have used a lot of different methods to solve these new problems. The goal of this paper is to understand basic theories and models of spatial econometric, model estimation methods, at last focusing on dynamic spatial econometric theories and models, then applied to the interpretation of the economic problems in our country.This paper first reviews the static spatial econometric theories and models, including: the spatial autoregressive model, spatial error model, spatial Durbin model, general spatial model, and spatial model using qualitative data, significance of spatial weighting matrix and its structure, the principle of maximum likelihood estimation method, as well as how to achieve this estimation process steps on the computer.The focus of the theoretical study of this article is dynamic spatial econometric models. We first study the type and classification of dynamic spatial econometric model with one endogeneous variable, especially the structure of the time-space dynamic spatial model. The feature of dynamic spatial econometric model is that the right side of the model contains explanatory variables lag term, and because of the panel data, time effects and fixed-effects also exist, and panel data inherent endogenous. To solve these problems, we study Quasi-Maximum Likelihood estimation method, its principles and process, as well as the nature of the estimators.In practice, we often encounter situations of multiple endogenous variables, so we introduce another spatial dynamic panel data model, is that the spatial panel VAR model. The variables in this model are all endogenous, the explanatory variables include its own lag period term, also contain the spatial effect that expressed by spatial weighted matrix. The most important problem about the estimation of such model is endogeneity, so this paper introduce GMM method to solve this problem, and this method is also used in the estimation of ordinary panel VAR model. We use the same GMM method to estimate the spatial panel VAR model, study its principles and processes, and finally achieve this process through computer programming. On this bais, we theoretically reveal the differences of spatial panel VAR model and panel VAR model, which reflects the throretical innovation. Our innovation embodies here, When we do the impulse response, all the source of impulse in ordinary panel VAR model come from the endogenous explanatory variables, and its response is same across all the cross sectionals. But in spatial panel VAR model, in addition to the endogenous variables, when the source of impulse happens in different regions, the response will be different. Because the shock will transfer and spread among these cross sections through the spatial weighting matrix, and the spatial distribution of every cross section is different, the impulse happens in different cross section will cause different effect.The practical innovation of this paper is using the two kinds of dynamic spatial econometric models to explain China’s actual economics phenomenon. Contrary to Chinese data characteristics and background, we construct a dynamic spatial econometric model about the fluctuations of pork price in China’s30provinces, we join spatial factors to explain the fluctuations of pork price. The results of the model show that the spatial effect on pork price is about0.85to0.90units. We also construct a spatial panel VAR model of total price level of China’s30provinces and cities, the model contain six endogeneous variables, these are consumer prices, food consumer prices, retail prices of commodities, food retail prices, producers’ prices for manufactured goods, raw materials, fuel and power purchase prices, we use this model to study the relationship of the interaction of the price volatility among the30provinces and cities. Its main conclusions is that the price in different province can be affect, from one market to another, even the same type of impulse but happen in different province, the response will also be different. From the point of view of literature, this conclusion is the first time to reflect the relationships and characteristics of the interactions among the different price indifferent provinces, basing on the spatial panel VAR model. In short, based on the detailed interpretation of spatial econometrics model, this article study the dynamic spatial econometric model with one and more than one endogeneous variables, and the spatial panel VAR model is the theoretical innovation. For our background and practical characteristics, this paper established a spatial dynamic model of the fluctuations of pork price of30provinces, and a spatial panel VAR model of different price indicators, estimated the impact of spatial effect on pork price, investigate the mutual interaction relationship and characteristics between different price in different province, the significance is that it reveals the effect of spatial distribution on economic phenomena, and present significant application innovation.