Spatial Econometric Model

In the traditional statistics, when we study the relationship between variables, we rarely consider about the spatial dependence, like classic time serial, and the panel data based on time serial and cross data. However, with the development of theoretical study and practical application, more and more scholars have began to pay attention to study the spatial dependence between variables.the concept of spatial dependence come from the dependence of time serial, and it shows the relativity of the same variable between one area and its neighboring area. For example, if there is similarity in the developing degree of the economic or education of two adjacent provinces, or they are right obverse, and so on. When studying the relativity, usually we need to erect some models that could show the character of spatial variables in order to study the relationship between variables. The normal models are Spatial Auto-regression model and Geographical weighted regression model. The nested format of spatial panel error weighted model and the geographical weighted regressive model that this thesis is going to study is just based on these two models.Spatial panel error weighted model is one kind of spatial regression model that was put forward by Kelejian and Robinson in 1993 and in 1995 separately. It suggested one new hypothesis to disturbing item based on the spatial model. Essentially, it is a hypothesis about if there is heterogeneity. This thesis combines it with panel data and considers the cross effect and spatial relationship between cross sections. It is more outstanding and has much more practical value comparing with the method that purely using spatial data and purely using panel data.Geographical weighted regressive model is one local spatial model and it embeds spatial geographical location in regression coefficients. It not only shows the relationship between independent variable and dependent variable, but also shows the spatial character of data. This thesis introduces the concept of zone based on this model, and divides the initial spatial area into different zone. Thus, when studying the spatial character of data, we consider the commonness of the same zone and heterogeneity of different zone.