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On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function

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Cited by:

  1. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
  2. Lin, Zhuo & Choo, Yap Y. & Oum, Tae H., 2013. "Efficiency Benchmarking of North American Airports: Comparative Results of Productivity Index, Data Envelopment Analysis and Stochastic Frontier Analysis," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 52(1).
  3. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
  4. Ley, Eduardo, 1992. "Switching regressions and activity analysis," UC3M Working papers. Economics 5820, Universidad Carlos III de Madrid. Departamento de Economía.
  5. Zhang, Feipeng & Li, Qunhua, 2017. "A continuous threshold expectile model," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 49-66.
  6. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
  7. Shangyu Xie & Yong Zhou & Alan T. K. Wan, 2014. "A Varying-Coefficient Expectile Model for Estimating Value at Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 576-592, October.
  8. Cullinane, Kevin & Song, Dong-Wook, 2006. "Estimating the Relative Efficiency of European Container Ports: A Stochastic Frontier Analysis," Research in Transportation Economics, Elsevier, vol. 16(1), pages 85-115, January.
  9. Victor Chernozhukov & Iv'an Fern'andez-Val & Tetsuya Kaji, 2016. "Extremal Quantile Regression: An Overview," Papers 1612.06850, arXiv.org, revised Feb 2017.
  10. Denis Chetverikov & Jesper R.-V. Sørensen, 2021. "Analytic and Bootstrap-after-Cross-Validation Methods for Selecting Penalty Parameters of High-Dimensional M-Estimators," Discussion Papers 21-04, University of Copenhagen. Department of Economics.
  11. Singh, Kehar & Dey, Madan Mohan & Rabbani, Abed G. & Sudhakaran, Pratheesh O. & Thapa, Ganesh, 2009. "Technical Efficiency of Freshwater Aquaculture and its Determinants in Tripura, India," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 22(2), July.
  12. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
  13. Philipp Gschöpf & Wolfgang Karl Härdle & Andrija Mihoci, 2015. "TERES - Tail Event Risk Expectile based Shortfall," SFB 649 Discussion Papers SFB649DP2015-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  14. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
  15. Awdesch Melzer & Wolfgang K. Härdle & Brenda López Cabrera, 2017. "Pricing Green Financial Products," SFB 649 Discussion Papers SFB649DP2017-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  16. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
  17. Girard, Stéphane & Guillou, Armelle & Stupfler, Gilles, 2013. "Frontier estimation with kernel regression on high order moments," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 172-189.
  18. Yundong Tu & Siwei Wang, 2023. "Variable Screening and Model Averaging for Expectile Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 574-598, June.
  19. Li, Qi, 1996. "Estimating a stochastic production frontier when the adjusted error is symmetric," Economics Letters, Elsevier, vol. 52(3), pages 221-228, September.
  20. Granger, Clive W.J. & Sin, Chor-yiu, 1999. "Modelling the Absolute Returns of Different Stock Indices: Exploring the Forecastability of an Alternative Measure of Risk," University of California at San Diego, Economics Working Paper Series qt48r4781r, Department of Economics, UC San Diego.
  21. Zhang, Yue-Jun & Ma, Shu-Jiao, 2019. "How to effectively estimate the time-varying risk spillover between crude oil and stock markets? Evidence from the expectile perspective," Energy Economics, Elsevier, vol. 84(C).
  22. Litimein, Ouahiba & Laksaci, Ali & Mechab, Boubaker & Bouzebda, Salim, 2023. "Local linear estimate of the functional expectile regression," Statistics & Probability Letters, Elsevier, vol. 192(C).
  23. Jeon, Jong-June & Kim, Yongdai & Won, Sungho & Choi, Hosik, 2020. "Primal path algorithm for compositional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
  24. Wang, Bingling & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "K-expectiles clustering," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  25. Belkacem Abdous & Bruno Remillard, 1995. "Relating quantiles and expectiles under weighted-symmetry," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(2), pages 371-384, June.
  26. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2022. "Spillover effects between commodity and stock markets: A SDSES approach," Resources Policy, Elsevier, vol. 79(C).
  27. Long Qian & Yunjie Zhou & Ying Sun, 2023. "Regional Differences, Distribution Dynamics, and Convergence of the Green Total Factor Productivity of China’s Cities under the Dual Carbon Targets," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
  28. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
  29. Mohammedi, Mustapha & Bouzebda, Salim & Laksaci, Ali, 2021. "The consistency and asymptotic normality of the kernel type expectile regression estimator for functional data," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
  30. Antonio Rubia Serrano & Lidia Sanchis-Marco, 2015. "Measuring Tail-Risk Cross-Country Exposures in the Banking Industry," Working Papers. Serie AD 2015-01, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  31. Randrianarisoa, Laingo Manitra & Bolduc, Denis & Choo, Yap Yin & Oum, Tae Hoon & Yan, Jia, 2015. "Effects of corruption on efficiency of the European airports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 79(C), pages 65-83.
  32. Kuosmanen, Timo & Zhou, Xun, 2021. "Shadow prices and marginal abatement costs: Convex quantile regression approach," European Journal of Operational Research, Elsevier, vol. 289(2), pages 666-675.
  33. Kim, Minjo & Lee, Sangyeol, 2016. "Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 1-19.
  34. Sudit, Ephraim F., 1995. "Productivity measurement in industrial operations," European Journal of Operational Research, Elsevier, vol. 85(3), pages 435-453, September.
  35. Cullinane, Kevin & Song, Dong-Wook & Gray, Richard, 2002. "A stochastic frontier model of the efficiency of major container terminals in Asia: assessing the influence of administrative and ownership structures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(8), pages 743-762, October.
  36. Osti, Davide, 2022. "Returns to scale with a Cobb-Douglas production function for four small Northern Italian firms," MPRA Paper 116351, University Library of Munich, Germany.
  37. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
  38. Denis Chetverikov & Jesper Riis-Vestergaard S{o}rensen, 2021. "Selecting Penalty Parameters of High-Dimensional M-Estimators using Bootstrapping after Cross-Validation," Papers 2104.04716, arXiv.org, revised Aug 2023.
  39. Cooper, W. W. & Kumbhakar, Subal & Thrall, Robert M. & Yu, Xuelin, 1995. "DEA and stochastic frontier analyses of the 1978 Chinese economic reforms," Socio-Economic Planning Sciences, Elsevier, vol. 29(2), pages 85-112, June.
  40. Zhao, Jun & Chen, Yingyu & Zhang, Yi, 2018. "Expectile regression for analyzing heteroscedasticity in high dimension," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 304-311.
  41. W. Cooper & C. Lovell, 2011. "History lessons," Journal of Productivity Analysis, Springer, vol. 36(2), pages 193-200, October.
  42. Tae-Hwy Lee & Aman Ullah & He Wang, 2019. "The Second-Order Asymptotic Properties of Asymmetric Least Squares Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 201-233, September.
  43. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
  44. Justin McCrary, 2007. "Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test," NBER Technical Working Papers 0334, National Bureau of Economic Research, Inc.
  45. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2021. "Systemic-systematic risk in financial system: A dynamic ranking based on expectiles," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 330-365.
  46. Aivazian, Sergei & Afanasiev, Mikhail & Rudenko, Victoria, 2014. "Analysis of dependence between the random components of a stochastic production function for the purpose of technical efficiency estimation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 3-18.
  47. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
  48. Collin Philipps, 2022. "Interpreting Expectiles," Working Papers 2022-01, Department of Economics and Geosciences, US Air Force Academy.
  49. Giuseppe Storti & Chao Wang, 2021. "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers 2104.04918, arXiv.org, revised Jul 2021.
  50. Nikolskiy, Ilya & Furmanov, Kirill, 2023. "Assessing the accuracy of efficiency rankings obtained from a stochastic frontier model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 128-142.
  51. Jun Zhao & Guan’ao Yan & Yi Zhang, 2022. "Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity," Statistical Papers, Springer, vol. 63(1), pages 1-28, February.
  52. Lina Liao & Cheolwoo Park & Hosik Choi, 2019. "Penalized expectile regression: an alternative to penalized quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 409-438, April.
  53. Duyen Nhat Lam Tran & Tien Dinh Nguyen & Thuy Thu Pham & Roberto F. Rañola & Thinh An Nguyen, 2021. "Improving Irrigation Water Use Efficiency of Robusta Coffee ( Coffea canephora ) Production in Lam Dong Province, Vietnam," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
  54. Jradi, Samah & Ruggiero, John, 2019. "Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier," European Journal of Operational Research, Elsevier, vol. 278(2), pages 385-393.
  55. Eliana Christou & Michael Grabchak, 2022. "Estimation of Expected Shortfall Using Quantile Regression: A Comparison Study," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 725-753, August.
  56. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
  57. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
  58. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
  59. Gounopoulos, Dimitrios & Kallias, Konstantinos & Newton, David & Tzeremes, Nickolaos, 2016. "Political connections and IPO underpricing: An efficiency problem," MPRA Paper 69427, University Library of Munich, Germany.
  60. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.
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