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dc.contributor.authorCheng, Xiaomei
dc.contributor.authorAndersson, Jonas
dc.contributor.authorBjørndal, Endre
dc.date.accessioned2015-09-17T10:46:50Z
dc.date.available2015-09-17T10:46:50Z
dc.date.issued2015-09-17
dc.identifier.issn1500-4066
dc.identifier.urihttp://hdl.handle.net/11250/300520
dc.description.abstractIn a recent paper Johnson and Kuosmanen (2011) propose a new, semi-parametric, general cost-frontier model, the stochastic nonparametric envelopment of data (StoNED). The model is semi-parametric in the sense that the cost function is estimated nonparametrically, while the functional form of the distribution for the error term is parametrically specified. A common assumption for this distribution is that it is a convolution of a truncated normal distribution, representing inefficiency, and a normal distribution, representing noise. This parametric form has the drawback that a negative skewness implies a negative expected inefficiency. It can thus never capture a negatively skewed distribution with a positive expectation. In this paper we investigate this assumption and its consequences for an analysis of inefficiency. Furthermore, we propose a solution to the problem and investigate its performance by means of a Monte Carlo simulation.nb_NO
dc.language.isoengnb_NO
dc.publisherFORnb_NO
dc.relation.ispartofseriesDiscussion paper;24/15
dc.subjectStoNED modelnb_NO
dc.subjectcomposite errornb_NO
dc.subjectwrong skewnessnb_NO
dc.subjectmisspecificationnb_NO
dc.titleOn the Distributional Assumptions in the StoNED modelnb_NO
dc.typeWorking papernb_NO


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