學術產出-Theses
Article View/Open
Publication Export
-
題名 考慮固定效果的隨機邊界模型概似函數之推導: Copula Functions之應用
The derivation of maximum likelihood function in fixed effect stochastic frontier model：an application of copula function作者 陳奕淙 貢獻者 黃台心
陳奕淙關鍵詞 隨機邊界法
固定效果模型
關聯結構函數
距離函數日期 2011 上傳時間 30-Oct-2012 11:24:14 (UTC+8) 摘要 Greene (2005) 在縱橫資料型態下提出真實固定效果隨機邊界模型 (true fixed effect stochastic frontier analysis, TFESFA)，該模型保留了傳統隨機邊界法之架構並考量到廠商間之異質性問題，同時設定廠商之無效率項可隨時間改變。但此模型假定不同廠商皆有特定之固定效果參數，當廠商家數多而資料觀察期間較少時，會因待估參數過多而導致模型存在擾攘參數問題，產生估計偏誤 。 本研究利用Tsay et al. (2009) 提出之方法，以錯誤函數 (error function) 之非線性近似函數以及關聯結構函數 (copula function) 推導得到TFESFA模型經一階差分轉換後組合誤差項之近似概似函數，成為本研究提出之差分隨機邊界模型(difference stochastic frontier model, DSFA) 模型，透過模擬過程生成平衡縱橫樣本及不平衡縱橫樣本，發現本研究提出之DSFA模型的確能在觀察期間較少時消除擾攘參數問題之影響。最後，本研究使用TFESFA模型及DSFA模型，配合投入面距離函數來衡量俄羅斯銀行之技術效率，而DSFA模型亦能達到更良好之估計效果。 參考文獻 一、中文文獻1.劉杏薇 (2002)，商業銀行逾放款之研究─應用距離函數法，暨南國際大學經濟研究所碩士論文2.李明宗 (2005)，考量風險與品質因素之銀行業效率分析：隨機投入距離函數法之應用，台北大學經濟研究所碩士論文3.張佩茹 (2008)，台灣上市櫃證券商經營效率與生產力變動之分析－隨機距離函數之應用，政治大學經濟研究所碩士論文二、英文文獻1.Aigner, D. J., C. A. K. Lovell and Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6, 21-37.2.Amsler, C., Prokhorov, A., and Schmidt, P. (2011). Using Copulas to Model time Dependence in Stochastic Frontier Models. Working Paper.3.Battese, G. E., and Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 31–32, 153–169.4.Battese, G. E., and Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325–332.5.Carta, A., and Steel, M. F. J. (2010). Modelling Multi-Output Stochastic Frontiers Using Copulas. Computational Statistics and Data Analysis, forthcoming.6.Caudill, S. B., Ford, J. M., and Gropper, D. M. (1995). Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity. Journal of Business and Economic Statistics, 13(1), 105-111.7.Charnes, A., Cooper, W. W., and Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research. Vol.2, 429-444. 8.Cornwell, C., Schmidt, P., and Sickles, R. (1990). Production Frontiers with Cross- Sectional and Time-Series Variation in Efficiency Levels. Journal of Econometrics, 46, 185–200.9.Cuesta, R. A., and Orea, L. (2002). Mergers and technical efficiency in Spanish savings banks: A stochastic distance function approach. Journal of Banking and Finance, 26(12), 2231–2247.10.Farrel, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society Series A CXX(Part 3), 253-281.11.Greene, W. (2005). Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity Analysis, 23, 7-32.12.Hadri, K., C. Guermat and Whittaker, J. (2003). Estimating farm efficiency in the presence of double heteroscedasticity using panel data. Journal of Applied Economics, Vol.VI, No.2, 255-268.13.Huang, H. C. (2004). Estimation of technical inefficiencies with heterogeneous technologies. Journal of Productivity Analysis, 21(3), 277-296.14.Karagiannis, G., Midmore, P., and Tzouvelekas, V. (2004). Parametric decomposition of output growth using a stochastic input distance function. American Journal of Agricultural Economics 86(4), 1044–1057.15.Kumbhakar, S. C. (1990). Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency. Journal of Econometrics, 46, 201-211.16.Lee, Y., and Schmidt, P. (1993). A Production Frontier Model with Flexible Temporal Variation in Technical Efficiency. In H. Fried and K. Lovell (eds.), The Measurement of Productive Efficiency: Techniques and Applications. New York: Oxford University Press.17.Marsh, T. L., Featherstone, A. M., and Garrett, T. A. (2003). Input inefficiency in commercial banks: A normalized quadratic input distance approach. Federal Reserve Bank of St. Louis, Working Paper 036A.18.Meeusen, W., and van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production function with composed error. International Economic Review 18, 435-444.19.Paul, C. J., Johnstion, W. E., and Frengley, A. G. (2000). Efficiency in New Zealand Sheep and Beef Farming: The Impacts of Regulatory Reform. The Review of Economics and Statistics, Vol.82, No.2, 325-337.20.Pitt, M., and Lee, L. (1981). The Measurement and Sources of Technical Inefficiency in Indonesian Weaving Industry. Journal of Development Economics, 9, 43-64.21.Schmidt, P. and Sickles, R. (1984). Production Frontiers with Panel Data. Journal of Business and Economic Statistics, 2 (4): 367–374.22.Smith, M. D. (2008). Stochastic frontier models with dependent error components. Econometrics Journal, Vol 11, 172-192.23.Tsay, W. J., Huang , C. J., Fu, T. T., and Ho, I. L. (2009). Maximum Likelihood Estimation of Censored Stochastic Frontier Models. TEAS, Working Paper No. 09-A003.24.Tsionas, E. G. (2002). Stochastic Frontier Models with Random Coefficients. Journal of Applied Econometrics , 17, 127-147.25.Wang, H., and Ho, C. (2010). Estimating fixed-effect panel stochastic frontier models by model transformation. Journal of Econometrics, 157,286-296. 描述 碩士
國立政治大學
金融研究所
99352014
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099352014 資料類型 thesis dc.contributor.advisor 黃台心 zh_TW dc.contributor.author (Authors) 陳奕淙 zh_TW dc.creator (作者) 陳奕淙 zh_TW dc.date (日期) 2011 en_US dc.date.accessioned 30-Oct-2012 11:24:14 (UTC+8) - dc.date.available 30-Oct-2012 11:24:14 (UTC+8) - dc.date.issued (上傳時間) 30-Oct-2012 11:24:14 (UTC+8) - dc.identifier (Other Identifiers) G0099352014 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54584 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融研究所 zh_TW dc.description (描述) 99352014 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) Greene (2005) 在縱橫資料型態下提出真實固定效果隨機邊界模型 (true fixed effect stochastic frontier analysis, TFESFA)，該模型保留了傳統隨機邊界法之架構並考量到廠商間之異質性問題，同時設定廠商之無效率項可隨時間改變。但此模型假定不同廠商皆有特定之固定效果參數，當廠商家數多而資料觀察期間較少時，會因待估參數過多而導致模型存在擾攘參數問題，產生估計偏誤 。 本研究利用Tsay et al. (2009) 提出之方法，以錯誤函數 (error function) 之非線性近似函數以及關聯結構函數 (copula function) 推導得到TFESFA模型經一階差分轉換後組合誤差項之近似概似函數，成為本研究提出之差分隨機邊界模型(difference stochastic frontier model, DSFA) 模型，透過模擬過程生成平衡縱橫樣本及不平衡縱橫樣本，發現本研究提出之DSFA模型的確能在觀察期間較少時消除擾攘參數問題之影響。最後，本研究使用TFESFA模型及DSFA模型，配合投入面距離函數來衡量俄羅斯銀行之技術效率，而DSFA模型亦能達到更良好之估計效果。 zh_TW dc.description.tableofcontents 第一章 緒論 ............................................ 1 1.1 研究動機與目的 .................................... 1 1.2 研究架構 ......................................... 2第二章 文獻回顧 ......................................... 3 2.1 效率之定義 ........................................ 3 2.2 隨機邊界模型相關文獻 ................................ 5 2.3 關聯結構函數相關文獻 ............................... 10 2.4 距離函數相關文獻 .................................. 11 2.5 文獻統整 ......................................... 13第三章 研究方法 ........................................... 17 3.1 研究模型介紹 ...................................... 17 3.2 差分隨機邊界模型 ................................... 19 3.2.1 模型定義 ..................................... 19 3.2.2 一階差分模型推導 ............................... 20 3.2.3 關聯結構函數之應用 ............................. 27 3.3 考慮環境變數之固定效果模型 ........................... 30第四章 模擬分析 ............................................ 38 4.1 平衡縱橫資料模擬 ................................... 38 4.2 不平衡縱橫資料模擬 ................................. 49 4.3 考慮環境變數之模擬分析............................... 54第五章 實證分析 ............................................ 57 5.1 實證模型介紹 ...................................... 57 5.2 資料處理及變數定義 ................................. 59 5.3 實證估計結果 ...................................... 60 5.4 穩健度分析 ........................................ 66第六章 結論及建議 .......................................... 72 6.1 結論 ............................................. 72 6.2 未來研究方向 ...................................... 73參考文獻 ................................................. 74 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099352014 en_US dc.subject (關鍵詞) 隨機邊界法 zh_TW dc.subject (關鍵詞) 固定效果模型 zh_TW dc.subject (關鍵詞) 關聯結構函數 zh_TW dc.subject (關鍵詞) 距離函數 zh_TW dc.title (題名) 考慮固定效果的隨機邊界模型概似函數之推導: Copula Functions之應用 zh_TW dc.title (題名) The derivation of maximum likelihood function in fixed effect stochastic frontier model：an application of copula function en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 一、中文文獻1.劉杏薇 (2002)，商業銀行逾放款之研究─應用距離函數法，暨南國際大學經濟研究所碩士論文2.李明宗 (2005)，考量風險與品質因素之銀行業效率分析：隨機投入距離函數法之應用，台北大學經濟研究所碩士論文3.張佩茹 (2008)，台灣上市櫃證券商經營效率與生產力變動之分析－隨機距離函數之應用，政治大學經濟研究所碩士論文二、英文文獻1.Aigner, D. J., C. A. K. Lovell and Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6, 21-37.2.Amsler, C., Prokhorov, A., and Schmidt, P. (2011). Using Copulas to Model time Dependence in Stochastic Frontier Models. Working Paper.3.Battese, G. E., and Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 31–32, 153–169.4.Battese, G. E., and Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325–332.5.Carta, A., and Steel, M. F. J. (2010). Modelling Multi-Output Stochastic Frontiers Using Copulas. Computational Statistics and Data Analysis, forthcoming.6.Caudill, S. B., Ford, J. M., and Gropper, D. M. (1995). Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity. Journal of Business and Economic Statistics, 13(1), 105-111.7.Charnes, A., Cooper, W. W., and Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research. Vol.2, 429-444. 8.Cornwell, C., Schmidt, P., and Sickles, R. (1990). Production Frontiers with Cross- Sectional and Time-Series Variation in Efficiency Levels. Journal of Econometrics, 46, 185–200.9.Cuesta, R. A., and Orea, L. (2002). Mergers and technical efficiency in Spanish savings banks: A stochastic distance function approach. Journal of Banking and Finance, 26(12), 2231–2247.10.Farrel, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society Series A CXX(Part 3), 253-281.11.Greene, W. (2005). Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity Analysis, 23, 7-32.12.Hadri, K., C. Guermat and Whittaker, J. (2003). Estimating farm efficiency in the presence of double heteroscedasticity using panel data. Journal of Applied Economics, Vol.VI, No.2, 255-268.13.Huang, H. C. (2004). Estimation of technical inefficiencies with heterogeneous technologies. Journal of Productivity Analysis, 21(3), 277-296.14.Karagiannis, G., Midmore, P., and Tzouvelekas, V. (2004). Parametric decomposition of output growth using a stochastic input distance function. American Journal of Agricultural Economics 86(4), 1044–1057.15.Kumbhakar, S. C. (1990). Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency. Journal of Econometrics, 46, 201-211.16.Lee, Y., and Schmidt, P. (1993). A Production Frontier Model with Flexible Temporal Variation in Technical Efficiency. In H. Fried and K. Lovell (eds.), The Measurement of Productive Efficiency: Techniques and Applications. New York: Oxford University Press.17.Marsh, T. L., Featherstone, A. M., and Garrett, T. A. (2003). Input inefficiency in commercial banks: A normalized quadratic input distance approach. Federal Reserve Bank of St. Louis, Working Paper 036A.18.Meeusen, W., and van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production function with composed error. International Economic Review 18, 435-444.19.Paul, C. J., Johnstion, W. E., and Frengley, A. G. (2000). Efficiency in New Zealand Sheep and Beef Farming: The Impacts of Regulatory Reform. The Review of Economics and Statistics, Vol.82, No.2, 325-337.20.Pitt, M., and Lee, L. (1981). The Measurement and Sources of Technical Inefficiency in Indonesian Weaving Industry. Journal of Development Economics, 9, 43-64.21.Schmidt, P. and Sickles, R. (1984). Production Frontiers with Panel Data. Journal of Business and Economic Statistics, 2 (4): 367–374.22.Smith, M. D. (2008). Stochastic frontier models with dependent error components. Econometrics Journal, Vol 11, 172-192.23.Tsay, W. J., Huang , C. J., Fu, T. T., and Ho, I. L. (2009). Maximum Likelihood Estimation of Censored Stochastic Frontier Models. TEAS, Working Paper No. 09-A003.24.Tsionas, E. G. (2002). Stochastic Frontier Models with Random Coefficients. Journal of Applied Econometrics , 17, 127-147.25.Wang, H., and Ho, C. (2010). Estimating fixed-effect panel stochastic frontier models by model transformation. Journal of Econometrics, 157,286-296. zh_TW
