Application and Analysis of Spatial Spillover Effects of an Improved Panel Data Econometric Model in Universities, Population, and Industrial Parks in Guiyang

Abstract

In the field of econometrics, panel data are an extremely important type of data. In macroeconomic research, panel data models are widely used in exchange rate determination theory, testing of cross-border economic growth and convergence theory, analysis of industrial structure, research on technological innovation, etc. The agglomeration and population distribution of colleges and universities, and the economic support of industrial parks play an important role in economic and social development as a source of research and development and a source of talents. The panel data model usually assumes that the error term follows a normal distribution, but the actual data are difficult to satisfy this assumption, and the estimation obtained by traditional methods may be biased or even invalid. This paper proposes a more robust and effective estimation method (ELS-EL) based on the panel data mean regression model, and extends this method to complex panel data models such as generalized linear models and partial linear models; in addition, this paper is based on panel data. We proposed a two-stage instrumental variable method (2S-IVFEQR) to reduce the computational complexity and generalized the new method to the quantile regression model of dynamic panel data. At the same time, this paper uses the above-improved panel data econometric model to analyze the spatial spillover effects of college aggregation, population distribution, and industrial parks in Guiyang. This study found that the agglomeration of colleges and universities has significantly promoted the economic growth of our country. These promotion effects come from both the direct contribution of college agglomeration and the positive external spillover effect of college agglomeration.