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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/54584


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    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: 2012-10-30 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
    顯示於類別:[金融學系] 學位論文

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