Risk programming and sparse data: how to get more reliable results
AbstractBecause 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.
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Bibliographic InfoPaper provided by European Association of Agricultural Economists in its series 2008 International Congress, August 26-29, 2008, Ghent, Belgium with number 44051.
Date of creation: 2008
Date of revision:
Risk programming; states of nature; sparse data; Risk and Uncertainty;
Other versions of this item:
- Lien, Gudbrand & Hardaker, J. Brian & Asseldonk, Marcel A.P.M. van & Richardson, James W., 2009. "Risk programming and sparse data: how to get more reliable results," Agricultural Systems, Elsevier, vol. 101(1-2), pages 42-48, June.
- NEP-ALL-2008-11-25 (All new papers)
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