【摘要】 为克服以往研究成果中注重全要素生产率测算的弊端,将PGM(1,1)模型与贝叶斯正则化神经网络相结合,建立了全要素生产率的PGM(1,1)—贝叶斯正则化神经网络组合预测模型,以中国全要素生产率预测为例,验证了模型的有效性和实用性,为研究全要素生产率开辟了新的思路.更多还原

【Abstract】 In order to overcome various defects brought by previous research which focus on factor productivity measures,grey neural network has been constructed to predict model of total factor productivity,which is based on the combination of PGM(1,1)model and Bayesian-regularization neural networks.Forecast with the factor productivity of china,has proved that the new method is feasible and has more excellent practical value,has opened up a new thoughts for the study of total factor productivity.It brings some new ideas and approaches for the factor productivity research.更多还原