Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions

Xinping Yang; Wei Zheng; Yunyuan Yang; Yanmei Li

doi:doi:10.11648/j.ajtas.20211002.14

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Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions

Xinping Yang, Wei Zheng, Yunyuan Yang, Yanmei Li

Published in American Journal of Theoretical and Applied Statistics (Volume 10, Issue 2)

Received: 19 March 2021 Accepted: 30 March 2021 Published: 12 April 2021

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 10, Issue 2)
DOI	10.11648/j.ajtas.20211002.14
Page(s)	122-128
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Generalized Production Function, Capital Stock, Non-random Missing, Gibbs Sampling, Impact Analysis

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APA Style

Xinping Yang, Wei Zheng, Yunyuan Yang, Yanmei Li. (2021). Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions. American Journal of Theoretical and Applied Statistics, 10(2), 122-128. https://doi.org/10.11648/j.ajtas.20211002.14

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ACS Style

Xinping Yang; Wei Zheng; Yunyuan Yang; Yanmei Li. Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions. Am. J. Theor. Appl. Stat. 2021, 10(2), 122-128. doi: 10.11648/j.ajtas.20211002.14

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AMA Style

Xinping Yang, Wei Zheng, Yunyuan Yang, Yanmei Li. Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions. Am J Theor Appl Stat. 2021;10(2):122-128. doi: 10.11648/j.ajtas.20211002.14

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@article{10.11648/j.ajtas.20211002.14,
  author = {Xinping Yang and Wei Zheng and Yunyuan Yang and Yanmei Li},
  title = {Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {2},
  pages = {122-128},
  doi = {10.11648/j.ajtas.20211002.14},
  url = {https://doi.org/10.11648/j.ajtas.20211002.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211002.14},
  abstract = {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.},
 year = {2021}
}

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TY  - JOUR
T1  - Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions
AU  - Xinping Yang
AU  - Wei Zheng
AU  - Yunyuan Yang
AU  - Yanmei Li
Y1  - 2021/04/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211002.14
DO  - 10.11648/j.ajtas.20211002.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 122
EP  - 128
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211002.14
AB  - 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.
VL  - 10
IS  - 2
ER  -

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Author Information

Xinping Yang

School of Mathematics and Statistics, Chuxiong Normal University, Chuxiong, China
Wei Zheng

School of Mathematics and Statistics, Chuxiong Normal University, Chuxiong, China
Yunyuan Yang

School of Geography and Tourist Management, Chuxiong Normal University, Chuxiong, China
Yanmei Li

School of Mathematics and Statistics, Chuxiong Normal University, Chuxiong, China

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Plain Text BibTeX RIS

APA Style

Xinping Yang, Wei Zheng, Yunyuan Yang, Yanmei Li. (2021). Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions. American Journal of Theoretical and Applied Statistics, 10(2), 122-128. https://doi.org/10.11648/j.ajtas.20211002.14

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ACS Style

Xinping Yang; Wei Zheng; Yunyuan Yang; Yanmei Li. Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions. Am. J. Theor. Appl. Stat. 2021, 10(2), 122-128. doi: 10.11648/j.ajtas.20211002.14

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AMA Style

Xinping Yang, Wei Zheng, Yunyuan Yang, Yanmei Li. Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions. Am J Theor Appl Stat. 2021;10(2):122-128. doi: 10.11648/j.ajtas.20211002.14

Copy | Download

@article{10.11648/j.ajtas.20211002.14,
  author = {Xinping Yang and Wei Zheng and Yunyuan Yang and Yanmei Li},
  title = {Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {2},
  pages = {122-128},
  doi = {10.11648/j.ajtas.20211002.14},
  url = {https://doi.org/10.11648/j.ajtas.20211002.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211002.14},
  abstract = {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.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Hierarchical Estimation and Impact Analysis of Generalized Production Functions
AU  - Xinping Yang
AU  - Wei Zheng
AU  - Yunyuan Yang
AU  - Yanmei Li
Y1  - 2021/04/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211002.14
DO  - 10.11648/j.ajtas.20211002.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 122
EP  - 128
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211002.14
AB  - 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.
VL  - 10
IS  - 2
ER  -

Copy | Download