Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models Abstract: In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise component and a one-sided inefficiency component, which is usually assumed half-normal, implying that the error distribution is skewed in one direction. In practice, however, estimation residuals may display skewness in the wrong direction. Model re-specification or pulling a new sample is often prescribed. This paper proposes a feasible alternative: imposing a negative skewness constraint on the residuals in maximum likelihood or corrected least squares estimation

Qu Feng, William Horrace and Guiying Wu ()

No 154, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Keywords: Stochastic Frontier Model; Skewness; MLE; Constrained Estimators (search for similar items in EconPapers)
JEL-codes: C13 C23 D24 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2013-02
New Economics Papers: this item is included in nep-acc, nep-pbe and nep-pub
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:154

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