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題名 運用貝氏方法估計方向距離函數─考慮環境變數、單調性與曲度限制下之效率分析
A Bayesian Approach to Imposing Monotonicity and Curvature on Directional Distance Function with Environmental Variables作者 林嘉偉
Lin, Chia-Wei貢獻者 黃台心
Huang, Tai-Hsin
林嘉偉
Lin, Chia-Wei關鍵詞 貝氏方法
方向距離函數
非意欲產出
單調性與曲度限制
環境變數
效率分數
技術進步率日期 2016 上傳時間 14-Nov-2016 16:09:52 (UTC+8) 摘要 本文以貝氏方法估計方向距離函數，加入單調性與曲度限制，同時考慮環境變數於模型中。為了凸顯考慮非意欲產出方向距離函數的優點，本文同時估計產出面射線距離函數，並與方向距離函數模型比較。實證分析資料為1970至2010年間各國總體經濟變數，分別在有無加入限制條件與環境變數的狀況下，估計兩種距離函數，從無效率值、效率分數與技術進步率等角度分析彼此間的差異。發現射線距離函數模型由於忽略非意欲產出，傾向高估生產單位的技術效率。另一方面，其係數估計值容易違反射線距離函數的先天性質，較不具參考性。而方向距離函數模型當中，有無加入限制條件與有無考慮環境變數的模型結果各不相同，其中同時加入限制條件與環境變數的模型結果最為合理。 參考文獻 Assaf, A.G., R. Matousek and E.G. Tsionas, (2013). Turkish bank efficiency: Bayesian estimation with undesirable outputs, Journal of Banking and Finance, 37, 506-517.Atkinson, S.E. and J.H. Dorfman, (2005). Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution, Journal of Econometrics, 126, 445-468.Battese, G.E. and T.J. Coelli, (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data, Empirical Economics, 20, 325-332.Breheny, P., (Retrieved September 26 2016). MCMC methods: Gibbs and Metropolis, from: http://web.as.uky.edu/statistics/users/pbreheny/701/S13/notes/2-28.pdfBokusheva, R. and S.C. Kumbhakar, (2014). A distance function model with good and bad outputs, from: http://ageconsearch.umn.edu/bitstream/182765/2/Bokusheva-Distance_function_model_with_good_and_bad_outputs-258_a.pdfBoyd, G.A., G. Tolley and J. Pang, (2002). Plant level productivity, efficiency, and environmental performance of the container glass industry, Environmental and Resource Economics, 23, 29-43.Broeck, J. van den, G. Koop, J. Osiewalski and M.F.J. Steel, (1994). Stochastic frontier models: a Bayesian perspective, Journal of Econometrics, 61, 273-303.Chambers, R.G., (2002). Exact nonradial input, output, and productivity measurement, Economic Theory, 20, 751-765.Chung, Y.H., R. Färe and S. Grosskopf, (1997). Productivity and undesirable outputs: a directional distance function approach, Journal of Environmental Management, 51, 229-240.Cowles, M.K. and B.P. Carlin, (Retrieved September 26 2016). Markov chain Monte Carlo convergence diagnostics: a comparative review, from: http://www.public.iastate.edu/~alicia/stat544/rr94-008.pdfFäre, R. and S. Grosskopf, (2003). New directions: efficiency and productivity, US: Springer.Färe, R., S. Grosskopf, D. Noh and W. Weber, (2005). Characteristics of a polluting technology, Journal of Econometrics, 126, 469-492.Feng, G. and A. Serletis, (2010). A primal Divisia technical change index based on the output distance function, Journal of Econometrics, 159, 320-330.Feng, G. and A. Serletis, (2014). Undesirable outputs and a primal Divisia productivity index based on the directional output distance function, Journal of Econometrics, 183, 135-146.Feng, G. and X.H. Zhang, (2012). Productivity and efficiency at large and community banks in the US: a Bayesian true random effects stochastic distance frontier analysis, Journal of Banking and Finance, 36, 1883-1895.Fernández, C., G.M. Koop and M. Steel, (2002). Multiple output production with undesirable outputs: an application to nitrogen surplus in agriculture, Journal of the American Statistical Association, 97, 432-442.Fernández, C., J. Osiewalski and M.F.J. Steel, (1997). On the use of panel data in stochastic frontier models with improper priors, Journal of Econometrics, 79,169-193.Flegal, J.M., (2008). Monte Carlo standard errors for Markov chain Monte Carlo, from: http://www.faculty.ucr.edu/~jflegal/Final_Thesis_twosided.pdfGriffin, J.E. and M.F.J. Steel, (2007). Bayesian stochastic frontier analysis using WinBUGS, Journal of Productivity Analysis, 27, 163-176.Griffin, J. E. and M.F.J. Steel, (2008). Flexible mixture modeling of stochastic frontiers, Journal of Productivity Analysis, 29, 33-50.Griffiths, W.E., C.J. O’Donnell and A.T. Cruz, (2000). Imposing regularity conditions on a system of cost and factor share equations, The Australian Journal of Agricultural and Resource Economics, 44, 107-127.Huang, C.J. and J.-T. Liu, (1994). Estimation of a non-neutral stochastic frontier production function, Journal of Productivity Analysis, 5, 171-180.Huang, H.C., (2004). Estimation of technical inefficiencies with heterogeneous technologies, Journal of Productivity Analysis, 21, 277-296.Huang, T.-H., (2005). A study on the productivities of IT capital and computer labor: firm-level evidence from Taiwan’s banking industry, Journal of Productivity Analysis, 24, 241-257.Kleit, A.N. and D. Terrell, (2001). Measuring potential efficiency gains from deregulation electricity generation: a Bayesian approach, Review of Economics and Statistics, 83, 523-530.Koop, G., J. Osiewalski and M.F.J. Steel, (1994b). Hospital efficiency analysis with individual effects: a Bayesian approach, Center for Economic Research discussion paper, 9447.Koop, G., J. Osiewalski and M.F.J. Steel, (1997). Bayesian efficiency analysis through individual effects: hospital cost frontiers, Journal of Econometrics, 76, 77-105.Kurkalova, L. A. and A. Carriquiry, (2003). Input- and output-oriented technical efficiency of Ukrainian collective farms, 1989-1992: Bayesian analysis of a stochastic production frontier model, Journal of Productivity Analysis, 20, 191-211.Lam, P., (Retrieved September 26 2016). MCMC methods: Gibbs sampling and the Metropolis-Hastings algorithm, from: http://pareto.uab.es/mcreel/IDEA2015/MCMC/mcmc.pdfLee, J.-D., J.-B. Park and T.-Y. Kim, (2002). Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach, Journal of Environmental Management, 63, 365-375.Lin, E.T.J. and L.W. Lan, (2010). Measuring firm-specific efficiencies with Bayesian stochastic distance function, 2010 International Conference on Asia Pacific Business Innovation and Technology Management.Morey, E.R., (1986). An introduction to checking, testing, and imposing curvature properties: the true function and the estimated function, Canadian Journal of Economics, 19, 207-235.O’Donnell, C.J. and T.J. Coelli, (2005). A Bayesian approach to imposing curvature on distance functions, Journal of Econometrics, 126, 493-523.Orea, L., (2002). Parametric decomposition of a generalized Malmquist productivity index, Journal of Productivity Analysis, 18, 5-22.Osiewalski, J. and M.F.J. Steel, (1998). Numerical tools for the Bayesian analysis of stochastic frontier models, Journal of Productivity Analysis, 10, 103-117.Sinharay, S., (2003). Assessing convergence of the Marlov chain Monte Carlo algorithms: a review, from: http://www.ets.org/Media/Research/pdf/RR-03-07-Sinharay.pdfTerrell, D., (1996). Incorporating monotonicity and concavity conditions in flexible functional forms, Journal of Applied Econometrics, 11, 179-194.Zhang, X., (1999). A Monte Carlo study on the finite sample properties of the Gibbs sampling method for a stochastic frontier model, Journal of Productivity Analysis, 14, 71-83. 描述 碩士
國立政治大學
金融學系
103352016資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103352016 資料類型 thesis dc.contributor.advisor 黃台心 zh_TW dc.contributor.advisor Huang, Tai-Hsin en_US dc.contributor.author (Authors) 林嘉偉 zh_TW dc.contributor.author (Authors) Lin, Chia-Wei en_US dc.creator (作者) 林嘉偉 zh_TW dc.creator (作者) Lin, Chia-Wei en_US dc.date (日期) 2016 en_US dc.date.accessioned 14-Nov-2016 16:09:52 (UTC+8) - dc.date.available 14-Nov-2016 16:09:52 (UTC+8) - dc.date.issued (上傳時間) 14-Nov-2016 16:09:52 (UTC+8) - dc.identifier (Other Identifiers) G0103352016 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/103978 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 103352016 zh_TW dc.description.abstract (摘要) 本文以貝氏方法估計方向距離函數，加入單調性與曲度限制，同時考慮環境變數於模型中。為了凸顯考慮非意欲產出方向距離函數的優點，本文同時估計產出面射線距離函數，並與方向距離函數模型比較。實證分析資料為1970至2010年間各國總體經濟變數，分別在有無加入限制條件與環境變數的狀況下，估計兩種距離函數，從無效率值、效率分數與技術進步率等角度分析彼此間的差異。發現射線距離函數模型由於忽略非意欲產出，傾向高估生產單位的技術效率。另一方面，其係數估計值容易違反射線距離函數的先天性質，較不具參考性。而方向距離函數模型當中，有無加入限制條件與有無考慮環境變數的模型結果各不相同，其中同時加入限制條件與環境變數的模型結果最為合理。 zh_TW dc.description.tableofcontents 第一章 緒論 1第一節 研究背景 1第二節 研究目的 3第三節 研究流程 4第二章 文獻回顧 5第一節 以貝氏方法估計距離函數 5第二節 環境變數 9第三章 研究方法 11第一節 方向距離函數 11第二節 射線距離函數 18第三節 貝氏方法 23第四節 環境變數 32第四章 資料與套裝軟體 38第一節 資料 38第二節 套裝軟體 42第五章 實證結果 44第一節 方向距離函數模型 44第二節 射線距離函數模型 67第三節 方向距離函數模型與射線距離函數模型比較 82第六章 結論與未來研究方向 84第一節 結論 84第二節 未來研究方向 85附錄 86參考文獻 93 zh_TW dc.format.extent 4789242 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103352016 en_US dc.subject (關鍵詞) 貝氏方法 zh_TW dc.subject (關鍵詞) 方向距離函數 zh_TW dc.subject (關鍵詞) 非意欲產出 zh_TW dc.subject (關鍵詞) 單調性與曲度限制 zh_TW dc.subject (關鍵詞) 環境變數 zh_TW dc.subject (關鍵詞) 效率分數 zh_TW dc.subject (關鍵詞) 技術進步率 zh_TW dc.title (題名) 運用貝氏方法估計方向距離函數─考慮環境變數、單調性與曲度限制下之效率分析 zh_TW dc.title (題名) A Bayesian Approach to Imposing Monotonicity and Curvature on Directional Distance Function with Environmental Variables en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Assaf, A.G., R. Matousek and E.G. Tsionas, (2013). Turkish bank efficiency: Bayesian estimation with undesirable outputs, Journal of Banking and Finance, 37, 506-517.Atkinson, S.E. and J.H. Dorfman, (2005). Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution, Journal of Econometrics, 126, 445-468.Battese, G.E. and T.J. Coelli, (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data, Empirical Economics, 20, 325-332.Breheny, P., (Retrieved September 26 2016). MCMC methods: Gibbs and Metropolis, from: http://web.as.uky.edu/statistics/users/pbreheny/701/S13/notes/2-28.pdfBokusheva, R. and S.C. Kumbhakar, (2014). A distance function model with good and bad outputs, from: http://ageconsearch.umn.edu/bitstream/182765/2/Bokusheva-Distance_function_model_with_good_and_bad_outputs-258_a.pdfBoyd, G.A., G. Tolley and J. Pang, (2002). Plant level productivity, efficiency, and environmental performance of the container glass industry, Environmental and Resource Economics, 23, 29-43.Broeck, J. van den, G. Koop, J. Osiewalski and M.F.J. Steel, (1994). Stochastic frontier models: a Bayesian perspective, Journal of Econometrics, 61, 273-303.Chambers, R.G., (2002). Exact nonradial input, output, and productivity measurement, Economic Theory, 20, 751-765.Chung, Y.H., R. Färe and S. Grosskopf, (1997). Productivity and undesirable outputs: a directional distance function approach, Journal of Environmental Management, 51, 229-240.Cowles, M.K. and B.P. Carlin, (Retrieved September 26 2016). Markov chain Monte Carlo convergence diagnostics: a comparative review, from: http://www.public.iastate.edu/~alicia/stat544/rr94-008.pdfFäre, R. and S. Grosskopf, (2003). New directions: efficiency and productivity, US: Springer.Färe, R., S. Grosskopf, D. Noh and W. Weber, (2005). Characteristics of a polluting technology, Journal of Econometrics, 126, 469-492.Feng, G. and A. Serletis, (2010). A primal Divisia technical change index based on the output distance function, Journal of Econometrics, 159, 320-330.Feng, G. and A. Serletis, (2014). Undesirable outputs and a primal Divisia productivity index based on the directional output distance function, Journal of Econometrics, 183, 135-146.Feng, G. and X.H. Zhang, (2012). Productivity and efficiency at large and community banks in the US: a Bayesian true random effects stochastic distance frontier analysis, Journal of Banking and Finance, 36, 1883-1895.Fernández, C., G.M. Koop and M. Steel, (2002). Multiple output production with undesirable outputs: an application to nitrogen surplus in agriculture, Journal of the American Statistical Association, 97, 432-442.Fernández, C., J. Osiewalski and M.F.J. Steel, (1997). On the use of panel data in stochastic frontier models with improper priors, Journal of Econometrics, 79,169-193.Flegal, J.M., (2008). Monte Carlo standard errors for Markov chain Monte Carlo, from: http://www.faculty.ucr.edu/~jflegal/Final_Thesis_twosided.pdfGriffin, J.E. and M.F.J. Steel, (2007). Bayesian stochastic frontier analysis using WinBUGS, Journal of Productivity Analysis, 27, 163-176.Griffin, J. E. and M.F.J. Steel, (2008). Flexible mixture modeling of stochastic frontiers, Journal of Productivity Analysis, 29, 33-50.Griffiths, W.E., C.J. O’Donnell and A.T. Cruz, (2000). Imposing regularity conditions on a system of cost and factor share equations, The Australian Journal of Agricultural and Resource Economics, 44, 107-127.Huang, C.J. and J.-T. Liu, (1994). Estimation of a non-neutral stochastic frontier production function, Journal of Productivity Analysis, 5, 171-180.Huang, H.C., (2004). Estimation of technical inefficiencies with heterogeneous technologies, Journal of Productivity Analysis, 21, 277-296.Huang, T.-H., (2005). A study on the productivities of IT capital and computer labor: firm-level evidence from Taiwan’s banking industry, Journal of Productivity Analysis, 24, 241-257.Kleit, A.N. and D. Terrell, (2001). Measuring potential efficiency gains from deregulation electricity generation: a Bayesian approach, Review of Economics and Statistics, 83, 523-530.Koop, G., J. Osiewalski and M.F.J. Steel, (1994b). Hospital efficiency analysis with individual effects: a Bayesian approach, Center for Economic Research discussion paper, 9447.Koop, G., J. Osiewalski and M.F.J. Steel, (1997). Bayesian efficiency analysis through individual effects: hospital cost frontiers, Journal of Econometrics, 76, 77-105.Kurkalova, L. A. and A. Carriquiry, (2003). Input- and output-oriented technical efficiency of Ukrainian collective farms, 1989-1992: Bayesian analysis of a stochastic production frontier model, Journal of Productivity Analysis, 20, 191-211.Lam, P., (Retrieved September 26 2016). MCMC methods: Gibbs sampling and the Metropolis-Hastings algorithm, from: http://pareto.uab.es/mcreel/IDEA2015/MCMC/mcmc.pdfLee, J.-D., J.-B. Park and T.-Y. Kim, (2002). Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach, Journal of Environmental Management, 63, 365-375.Lin, E.T.J. and L.W. Lan, (2010). Measuring firm-specific efficiencies with Bayesian stochastic distance function, 2010 International Conference on Asia Pacific Business Innovation and Technology Management.Morey, E.R., (1986). An introduction to checking, testing, and imposing curvature properties: the true function and the estimated function, Canadian Journal of Economics, 19, 207-235.O’Donnell, C.J. and T.J. Coelli, (2005). A Bayesian approach to imposing curvature on distance functions, Journal of Econometrics, 126, 493-523.Orea, L., (2002). Parametric decomposition of a generalized Malmquist productivity index, Journal of Productivity Analysis, 18, 5-22.Osiewalski, J. and M.F.J. Steel, (1998). Numerical tools for the Bayesian analysis of stochastic frontier models, Journal of Productivity Analysis, 10, 103-117.Sinharay, S., (2003). Assessing convergence of the Marlov chain Monte Carlo algorithms: a review, from: http://www.ets.org/Media/Research/pdf/RR-03-07-Sinharay.pdfTerrell, D., (1996). Incorporating monotonicity and concavity conditions in flexible functional forms, Journal of Applied Econometrics, 11, 179-194.Zhang, X., (1999). A Monte Carlo study on the finite sample properties of the Gibbs sampling method for a stochastic frontier model, Journal of Productivity Analysis, 14, 71-83. zh_TW
