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Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions

机译:广义生产函数的贝叶斯分层估算与影响分析

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Bayesian model is constructed to estimate the missing data of capital stock, and the impact of three industrial structures on the tertiary industry is discussed. The results show that: the maximum MC error with missing data on capital stock estimated by Bayesian is 0.4963, the maximum MC error of production function estimated by Bayesian stratation is 0.3276, the standard deviation is 0.0890 and the accuracy is high; from 1993 to 2018, the sum of capital output elasticity and labor output elasticity in the tertiary industry in Yunnan Province was greater than 1, and the scale compensation was increasing; the level of technological progress, the growth rate of all factors, the elasticity of capital output and the elasticity of labor output were all close to stable, and the ranges of changes were 0.2714-0.3252, -0.0680-0.0390, 0.5615-0.5858 and 0.4522-0.4784, respectively; the elasticity of capital output was greater than that of labor force output, and the tertiary industry in Yunnan Province was more dependent on the elasticity of capital output.
机译:贝叶斯模型被建造起来估计资本股票缺失的数据,并讨论了三个工业结构对第三产业的影响。结果表明:贝叶斯估计的资本股票缺失数据的最大MC错误是0.4963,由贝叶斯分层估计的生产函数的最大MC误差为0.3276,标准偏差为0.0890,精度高。从1993年到2018年,云南省第三产业的资本产出弹性和劳动力产量弹性的总和大于1,规模赔偿日益增加;技术进步水平,所有因素的增长率,资本产出的弹性和劳动力产量的弹性都均接近稳定,而变化的范围为0.2714-0.3252,-0.0680-0.0390,0.5615-0.5858和0.4522 -0.4784分别;资本产出的弹性大于劳动力产出的弹性,云南省的第三产业更加依赖资本产出的弹性。

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