Nonparametric Tests of Tail Behavior in Stochastic Frontier Models
William Horrace () and
Yulong Wang ()
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Yulong Wang: Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244, https://www.maxwell.syr.edu/directory/yulong-wang
No 230, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University
This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.
Keywords: Hypothesis Testing; Production; Inefficiency; Deconvolution; Extreme Value Theory (search for similar items in EconPapers)
JEL-codes: C12 C21 D24 (search for similar items in EconPapers)
Pages: 36 pages
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
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Journal Article: Nonparametric tests of tail behavior in stochastic frontier models (2022)
Working Paper: Nonparametric Tests of Tail Behavior in Stochastic Frontier Models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:230
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