Economics at your fingertips  

Production Analysis with Asymmetric Noise

Oleg Badunenko and Daniel Henderson

MPRA Paper from University Library of Munich, Germany

Abstract: Symmetric noise is the prevailing assumption in production analysis, but it is often violated in practice. Not only does asymmetric noise cause least-squares models to be inefficient, it can hide important features of the data which may be useful to the firm/policymaker. Here we outline how to introduce asymmetric noise into a production or cost framework as well as develop a model to introduce inefficiency into said models. We derive closed-form solutions for the convolution of the noise and inefficiency distributions, the log-likelihood function, and inefficiency, as well as show how to introduce determinants of heteroskedasticity, efficiency and skewness to allow for heterogenous results. We perform a Monte Carlo study and profile analysis to examine the finite sample performance of the proposed estimators. We outline R and Stata packages that we have developed and apply to three empirical applications to show how our methods lead to improved fit, explain features of the data hidden by assuming symmetry, and how our approach is still able to estimate efficiency scores when the least-squares model exhibits the well-known "wrong skewness" problem in production analysis.

Keywords: asymmetry; production; cost; efficiency; wrong skewness (search for similar items in EconPapers)
JEL-codes: C13 C21 D24 I21 (search for similar items in EconPapers)
Date: 2021-11-31
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

Page updated 2023-06-17
Handle: RePEc:pra:mprapa:110888