Estimation of production risk and risk preference function: a nonparametric approach

Subal Kumbhakar and Mike Tsionas

Annals of Operations Research, 2010, vol. 176, issue 1, 369-378

Abstract: While estimating parametric production models with risk, one faces two main problems. The first problem is associated with the choice of functional forms on the mean production function and the risk (variance) function. The second problem is associated with the specification of the risk preference function. In a parametric model the researcher chooses some ad hoc functional form on all these. It is obvious that the estimated (i) technology (mean production function), (ii) risk and (iii) risk preference functions are affected by the choice of functional form. In this paper we consider an estimation framework that avoids assuming parametric functions on all three. In particular, this paper deals with nonparametric estimation of the technology, risk and risk preferences of producers when they face uncertainty in production. Uncertainty is modeled in the context of production theory where producers’ maximize expected utility of anticipated profit. A multi-stage nonparametric estimation procedure is used to estimate the production function, the output risk function and the risk preference function. No distributional assumption is made on the random term representing production uncertainty. No functional form is assumed on the underlying utility function. Rice farming data from Philippines are used for an empirical application of the proposed model. Rice farmers are, in general, found to be risk averse; labor is risk decreasing while fertilizer, land and materials are risk increasing. The mean risk premium is about 3% of mean profit. Copyright Springer Science+Business Media, LLC 2010

Keywords: Production risk; Risk preference function; Risk premium; Kernel method (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (26)

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DOI: 10.1007/s10479-008-0472-5

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