Dissertation
Dissertation > Social Sciences > Statistics > Statistical methods

The Research on R&D Effect of High-tech Industries

Author GaoJiaFeng
Tutor QianZhengMing
School Xiamen University
Course Statistics
Keywords High-tech Industy R&D effect Bayesian Hierarchical Methods State-Space Model
CLC C81
Type Master's thesis
Year 2014
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Since the last century, China’s high-tech industry has been rapid development. Until2011, the total output value has stood on a great status, but the intensity of R&D investment is behind many countries. In the process of modernization of the country, an important engine of economic growth and performance is R&D investment and its contribution to the high-tech industry. Thus, it is significant to study in deep in the view of macro regional environmental and micro-enterprises’ investment.In this paper, based on the data from high-tech in east, middle, west and30provinces from2007to2012, we select the ratio of Profit before tax and total output as the dependent variable, the R&D input as in dependent variables. We select the ratio of technology expenses and expenditure in each province as the second-level independent variable, and set Bayesian Hierarchical Methods. Then, we use R and Winbugs software to fit the State-Space Model and Bayesian Hierarchical Methods. After the analysis, we draw the conclusion:(1) The R&D investment has a lag effect on output.(2)Not only R&D investment but also Environment factor influence the output.(3) From the vertical development perspective, the total output value of China’s high-tech industry interest rate has a significant positive growth effect.(4) R&D investment and Environment factor influence output in many ways at the same time.To sum up, there are some innovations following as:Firstly, we select vertical development model fit the data, and take the prior information which reflects the high-tech industry developmen into consideration, to estimate the fixed effects and random effects in model systems. In the end, we compare the conclusions of common HLM and Bayesian Hierarchical Methods.Secondly, according to the characteristics of panel data, we combine with state-space model and Bayesian Hierarchical Methods to analysis the effect of R&D investment in high-tech industries. It’s the first time combining State-Space and Bayesian Hierarchical Methods to analyze the high-tech industry’s R&D effects.Thirdly, we study R&D effect of China’s high-tech industry in deep in the macro perspective, micro perspective as well as dynamic development perspective.

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