A Spatial Statistics Research On The Innovation Capability Of China’s Regions

In the era of knowledge economy, the regional innovation capability is the decisive factor which can help the regional economic to gain competitive advantage. So improving the regional innovation capability is the important way to realize regional development, which is the major issues that is wildly focused. Therefore, the research on discussing the strategies of evaluation and promotion of the regional innovation capacity is of great importance with new visual angle.The objective of this paper is to provide empirical evidence on studying the innovation capacity of China’s regions in spatial statistics and econometric analysis. This paper at first summaries literature about the regional innovation capacity and reviews the research situation. Moreover, it reviews the theoretical source and the theoretical framework the research is based on, of regional innovation capacity in order to understand the regional innovation capacity better.Then, this article uses Exploratory Spatial Data Analysis (ESDA) to carry out global and local space-correlated test for provincial innovation output agglomeration. According to the test results, This paper uses Spatial Lag Model (SLM), Spatial Error Model (SEM) and Geographical Weighted Regression (GWR) in the spatial econometric analysis on the influences of the innovation capability of China’s regions,which is based on modified National Innovation Capability Framework and China’s provincial patent data and some economics data in year2000to2009.Finally, this paper summarizes the main empirical results and proposes some suggestion to the influences of innovation capacity of China’s regions.The main results of this paper is that, the activities of China’s provincial innovation output have a global and local spatial correlation.On that basis, analyzing the structural models and influences of the regional innovation capability, whether using Patent application or Patent grant, no matter how long the time lag is, find that the results are similar and relatively reliable. When making a gobal estimation, we find that the Spatial Lag Model is a better one that is the regional innovation has significant spillover effect. While making a local estimation, the Geographical Weighted Regression Model reveals the different situation all the factors influence provincial innovation capability agglomeration. The result of influencing factors analysis shows that, at the beginning, the main factors which influence China’s provincial innovation capability agglomeration are accumulated stock of knowledge, regional r&d resources input, interregional technology spillover and Knowledge spillover effect, while in the end these factors become accumulated stock of knowledge, regional r&d resources input, industrial structure, cluster environment and interregional technology spillover.