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 |
Generalized Production Function, Capital Stock, Non-random Missing, Gibbs Sampling, Impact Analysis
| [1] | Hang shuoshuo. An empirical analysis of the development of the tertiary industry in Shandong Province—Based on Cobb-Douglas production function [J]. Caixun, 2017, (4): 107-109. |
| [2] | Wang ziqing. An empirical analysis of the development of tertiary industry in Anhui Province Based on production function [J]. Modern enterprise culture, 2012, (7): 125-127. |
| [3] | Feng liying, Feng ruiqin, Li haixia. The production efficiency measurement of Inner Mongolia service industry Based on Cobb-Douglas production function [J]. Journal of Inner Mongolia University of Finance and Economics, 2015, 2 (13): 9-14. |
| [4] | Wang jun, Liu lang-juan. Estimation of Elasticity of Substitution in CES function of major Industries in Shanghai [J]. Shanghai Journal of Economics, 2012, (5): 106-112. |
| [5] | Zeng zhao-fa, Mi xian-hua. The Estimation and empirical of extended C-D production function based on Bayesian panel model [J], Statistic & Iformation Forum, 2014, 7 (29): 29-34. |
| [6] | Shi lei, Xiang qi-feng, Cheng fei. Multilevel model and its application in Economics [M]. China science press, 2013. |
| [7] | Liu wei, Li shao-rong. Industrial structure and economic growth [J]. China industrial economy, 2002, (5): 1-8. |
| [8] | Rubin D B. Inference and missing data [J]. Biometrika, 1976, 63 (3): 581-592. |
| [9] | Molenberghs, Kenward M G. Missing data in clinical stydies [M]. Joha Wiley & Sons, New York, 2008. |
| [10] | Daniels M J, Hogan J W. Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analtsis [M]. Chapman & Hall/CRC, Boca Raton, FL, 2008. |
| [11] | Lunn D, Jackson Christopher, Best Nicky, et al. A practical introduction to bayesian Analysis [M]. Chapman & Hall/CRC Press, 2013. |
| [12] | Liu da-peng. Re-estimation of capital stock: Yunnan case [J]. 2016, (7): 244-245. |
| [13] | Ioannis Ntzoufras. Bayesian modeling using WinBUGS [M]. Wiley, Hoboken, NJ, 2008. |
| [14] | Yang yun-yuan, Yang xin-ping, Zhang jie. Bayesian estimation of domestic covid-19 epidemic data based on Dirichlet model [J]. Journal of Sichuan normal university (natural science). 2020, 11 (43): 838-846. |
| [15] | Li ya, Li zhi-peng. Econometric analysis and Eviews [M]. China social sciences press, 2017. |
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
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
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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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}
}
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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