Specification Testing of Production in a Stochastic Frontier Model

Xu Guo, Gao-Rong Li, Michael McAleer and Wing-Keung Wong

No EI 2017-27, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Parametric production frontier functions are frequently used in stochastic frontier models, but there do not seem to be any empirical test statistics for its plausibility. To bridge the gap in the literature, we develop two test statistics based on local smoothing and an empirical process, respectively. Residual-based wild bootstrap versions of these two test statistics are also suggested. The distributions of technical inefficiency and the noise term are not specified, which allows specification testing of the production frontier function even under heteroscedasticity. Simulation studies and a real data example are presented to examine the finite sample sizes and powers of the test statistics. The theory developed in this paper is useful for production mangers in their decisions on production.

Keywords: Production frontier function; Stochastic frontier model; Specification testing; Wild bootstrap; Smoothing process; Empirical process; Simulations (search for similar items in EconPapers)
JEL-codes: C0 C13 C14 D81 (search for similar items in EconPapers)
Pages: 22
Date: 2017-01-01
New Economics Papers: this item is included in nep-ore
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Related works:
Journal Article: Specification Testing of Production in a Stochastic Frontier Model (2018)
Working Paper: Specification Testing of Production in a Stochastic Frontier Model (2017)
Working Paper: Specification Testing of Production in a Stochastic Frontier Model (2017)
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