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

J.B. Hardaker, G. Lien, M.A.P.M. van Asseldonk, W. Richardson, A. Hegrenes

Research output: Contribution to conferenceConference paperAcademic

Abstract

Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations. We 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 riskbased models. For the particular example of a Norwegian mixed livestock and crop farm, the solution is shown to be unstable with few states, although the cost of picking a sub-optimal plan declines with increases in number of states by Latin Hypercube sampling.
Original languageEnglish
Publication statusPublished - 2008
Event12th Congress of the European Association of Agricultural Economists -
Duration: 26 Aug 200829 Aug 2008

Conference

Conference12th Congress of the European Association of Agricultural Economists
Period26/08/0829/08/08

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