Stochastic frontier models with threshold efficiency

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• 作者：Sungwon Lee (1)
  Young Hoon Lee (1)
  
• 关键词：Stochastic frontier ; Technical efficiency ; Threshold inefficiency ; Uniform distribution ; Productivity distribution ; C13 ; C21 ; D24 ; L11
• 刊名：Journal of Productivity Analysis
• 出版年：2014
• 出版时间：August 2014
• 年：2014
• 卷：42
• 期：1
• 页码：45-54
• 全文大小：250 KB
• 参考文献：1. Aigner DJ, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21-7 CrossRef
  2. Almanidis P, Sickles R (2010) The skewness problem in stochastic frontier models : fact or fiction?, forthcoming in Exploring Research Frontiers in Contemporary Statistics and Econometrics: A Festschrift in Honor of Leopold Simar, Ingrid Van Keilegom and Paul Wilson (eds), Springer Publishing
  3. Amemiya T (1973) Regression analysis when the dependent variable is truncated normal. Econometrica 41(6):997-016 CrossRef
  4. De Loecker J (2009) Product differentiation, multi-product firms and estimating the impact of trade liberalization on productivity, / Working Paper, Princeton University
  5. Dunne T, Klimek S, Schmitz JA (2008) Does Foreign competition spur productivity? evidence from Post WWII US cement manufacturing, / Working Paper, Federal Reserve Bank Minneapolis
  6. Feng Q, Horrace WC (2012) Alternative technical efficiency measures: skew, bias and scale. J Appl Econom 27:253-68 CrossRef
  7. Good DH, Nadiri MI, Roller L-H, Sickles RC (1993) Efficiency and roductivity growth comparison of European and US air carriers: a first look at the data. J Prod Anal 4:115-25 CrossRef
  8. Greene WH (2008) The econometric approach to efficiency analysis. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency and productivity growth. Oxford University Press, Oxford
  9. Holmes TJ, Schmitz JA (2001) Competition at work: Railroad vs. monopoly in US shipping. Fed Reserve Bank Minneap Q Rev 19(1):3-9
  10. Holmes TJ, Schmitz JA (2010) Competition and productivity: a review of evidence. Annu Rev Econ 2:619-42 CrossRef
  11. Jondrow J, Lovell C, Materov I, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econom 19:233-38 CrossRef
  12. Lee YH (1996) Tail truncated stochastic frontier models. J Econ Theory Econom 2:137-52
  13. Matsa D (2011) Competition and product quality in the supermarket industry. Q J Econ 126(3):1539-591
  14. Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Intern Econ Rev 18:435-55 CrossRef
  15. Qian J, Sickles R (2008) Stochastic frontiers with bounded inefficiency, mimeo, Rice University
  16. Schmidt P, Sickles R (1984)?Production frontiers and panel data. J Bus Econ Stat 2(4):367-74
  17. Stevenson RE (1980) Generalized stochastic frontier estimation. J Econom 13:57-6
  18. Syverson C (2004) Market structure and productivity : a concrete example. J Polit Econ 112:1181-222 CrossRef
  
• 作者单位：Sungwon Lee (1)
  Young Hoon Lee (1)
  
  1. Sogang University, Seoul, South Korea
  
• ISSN：1573-0441

文摘

This paper proposes a tail-truncated stochastic frontier model that allows for the truncation of technical efficiency from below. The truncation bound implies the inefficiency threshold for survival. Specifically, this paper assumes a uniform distribution of technical inefficiency and derives the likelihood function. Even though this distributional assumption imposes a strong restriction that technical inefficiency has a uniform probability density over [0, θ], where θ is the threshold parameter, this model has two advantages: (1) the reduction in the number of parameters compared with more complicated tail-truncated models allows better performance in numerical optimization; and (2) it is useful for empirical studies of the distribution of efficiency or productivity, particularly the truncation of the distribution. The Monte Carlo simulation results support the argument that this model approximates the distribution of inefficiency precisely, as the data-generating process not only follows the uniform distribution but also the truncated half-normal distribution if the inefficiency threshold is small.