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A finite sample improvement of the fixed effects estimator: applied to technical infficiency
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0003-4378-9593
2013 (English)Report (Other academic)
Abstract [en]

The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.

Place, publisher, year, edition, pages
2013. , 41 p.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2013:13
National Category
Research subject
Complex Systems – Microdata Analysis
URN: urn:nbn:se:du-12151OAI: diva2:617544
Available from: 2013-04-23 Created: 2013-04-23 Last updated: 2015-11-10Bibliographically approved

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WP1_DW(471 kB)143 downloads
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ReferencesLink to record
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