地级市R&D知识溢出的GWR实证分析

An Empirical Analysis on China's Regional R&D Knowledge Spillovers on Using GWR

本文运用地理加权回归(geographical weighted regression,GWR)方法,对1997-2002年期间中国地级市R&D知识溢出的空间非稳定性进行了实证分析。传统的OLS只是对参数进行"平均"或"全局"估计,不能反映参数在不同空间的空间非稳定性;GWR是一个简单、有效的技术,可以反映参数在不同空间的空间非稳定性。研究结果发现:在对R&D知识生产进行参数估计时,GWR模型与OLS模型有显著的差异;R&D知识生产的不同要素存在空间变异。

The present paper employs technique of geographical weighted regression （GWR） to make an empirical study of China＇s R＆D knowledge spillovers at city level. Conventional regression analysis can only produce ＇average＇ and ＇global＇parameter estimates rather than ＇local＇ parameter estimates which vary over space in some spatial systems. Geographically weighted regression （GWR）, on the other hand, is a simple, but useful new technique for the analysis of spatial nonstationarity. Results show that there is a significant difference between OLS and GWR in estimating the parameters of R＆D knowledge production, and that the relationships between level of regional innovation activities and various factors show considerable spatial variability.