基于Translog模型对中国汽车业规模报酬及技术进步的分析

Analysis of Chinese auto industry scale pay and the progress of technology based on Translog model

利用中国汽车产业2000～2009年的时间序列数据,将影响汽车产业规模报酬的因素分为3个方面:技术工人、非技术工人和资本。建立汽车产业Translog生产函数模型进行估计,并经过BP神经网络学习算法得到十余年间汽车产业规模报酬的大趋势,从而发现现阶段中国汽车产业规模报酬并未达到理想状态,说明汽车产业的生产规模仍然存在较大的可发展空间。

Using China＇s automobile industry time series data among 2000-2009, the paper divides influence fac- tors of the car industry scale pay into three aspects： technology workers, unskilled workers and capital. Then, es- tablishing a model of Translog auto industry production function to estimate, and using the BP neural network learn- ing algorithm to find big trend of the auto industry scale beyond 10 years, further more finds China＇s automobile in- dustry scale not to achieve the ideal state at present , still has the big space to develop.