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Stochastic production frontiers with multiply imputed German establishment data

Authors: Jensen, U.; Rässler, Susanne;

Stochastic production frontiers with multiply imputed German establishment data

Abstract

In this paper, stochastic production frontier models are estimated with IAB establishment data from waves 2002 and 2003 to analyze productivity and inefficiency. The data suffer from nonresponse in the most important variables (output, capital and labor) leading to the loss of 25 % of the observations and possibly imprecise estimates and invalid test statistics. Therefore the missing values are multiply imputed. The analysis of the estimation results shows that, particularly in the inefficiency submodel, working with multiply imputed data reveals some interesting and plausible results which are not available when missing observations are ignored.

Country
Germany
Related Organizations
Keywords

IAB-Betriebspanel, Fehler, ddc:330, IAB-Betriebspanel, Schätzung, Fehler, Datenaufbereitung, angewandte Statistik, Datenaufbereitung, angewandte Statistik, Schätzung

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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