Risk programming and sparse data: how to get more reliable results

Gudbrand Lien, J. Brian Hardaker, Marcel A.P.M. van Asseldonk and James Richardson ()

Agricultural Systems, 2009, vol. 101, issue 1-2, 42-48

Abstract: Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations of uncertain crop and livestock returns. We show the instability of model solutions with few observations and discuss how to use available information to derive an appropriate multivariate distribution function that can be sampled for a more complete representation of the possible risks in risk-based models. For the particular example of a Norwegian mixed livestock and crop farm, the solution is shown to be unstable with few states of nature producing a risky solution that may be appreciably sub-optimal. However, the risk of picking a sub-optimal plan declines with increases in number of states of nature generated by Latin hypercube sampling.

Keywords: Risk; programming; States; of; nature; Sparse; data; Kernel; smoothing; Latin; hypercube; sampling (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Working Paper: Risk programming and sparse data: how to get more reliable results (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:101:y:2009:i:1-2:p:42-48

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