Model Averaging Estimators for the Stochastic Frontier Model

Christopher Parmeter, Alan Wan and Xinyu Zhang
Additional contact information
Alan Wan: City University of Hong Kong
Xinyu Zhang: Academy of Mathematics and Systems Science

No 2016-09, Working Papers from University of Miami, Department of Economics

Abstract: Given the concern over the impact of distributional assumptions for the stochastic frontier model, the present research proposes two distinct model averaging estimators, one which can average over distinct distributions for firm specific inefficiency and another that can average over a nested class of distributions. These estimators are shown to produce optimal weights when the criterion is to uncover conditional inefficiency. We study the finite-sample performance of the model average estimator via Monte Carlo experiments and an empirical application focusing on Philippines rice farming.

Keywords: Jackknife; optimal; focus criterion; leave-one-out Publication Status: Under Review (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2016-12-06
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://www.herbert.miami.edu/_assets/files/repec/WP2016-09.pdf First version, 2016 (application/pdf)

Related works:
Journal Article: Model averaging estimators for the stochastic frontier model (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:mia:wpaper:2016-09

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