Abstract
Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio-temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio-temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K -fold crossvalidation method is adopted to evaluate the prediction effect under the data-driven idea. This set of spatio-temporal data mining methods will be used to measure the technical efficiency of high-tech industries in various provinces of China.
Highlights
Stochastic frontier analysis (SFA) is an important method to measure technical efficiency and calculate total factor productivity
The above two characteristics lead to the large deviation of the traditional stochastic frontier model when analyzing the spatio-temporal data, and it is impossible to make an accurate measure of the production efficiency with spatial relationship, either
This paper proposes the spatiotemporal stochastic frontier model; considering that the model may be endogenous in time and space dimensions, a generalized method of moments (GMM) estimation process is designed to estimate the model
Summary
Stochastic frontier analysis (SFA) is an important method to measure technical efficiency and calculate total factor productivity. The paper found that the traditional stochastic frontier analysis method has the following defects: (a) it is not suitable for the special structure of spatial data or spatio-temporal data;. The above two characteristics lead to the large deviation of the traditional stochastic frontier model when analyzing the spatio-temporal data, and it is impossible to make an accurate measure of the production efficiency with spatial relationship, either. In view of the unique structure of spatio-temporal data, a more suitable crossvalidation method is proposed for the selection of prediction model. The weight matrix is constructed by geographical distance between regions, and this W2.
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