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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farms
livestock
crops
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Cite this

Hardaker, J. B., Lien, G., van Asseldonk, M. A. P. M., Richardson, W., & Hegrenes, A. (2008). Risk programming and sparse data: how to get more reliable results. Paper presented at 12th Congress of the European Association of Agricultural Economists, .
Hardaker, J.B. ; Lien, G. ; van Asseldonk, M.A.P.M. ; Richardson, W. ; Hegrenes, A. / Risk programming and sparse data: how to get more reliable results. Paper presented at 12th Congress of the European Association of Agricultural Economists, .
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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.",
author = "J.B. Hardaker and G. Lien and {van Asseldonk}, M.A.P.M. and W. Richardson and A. Hegrenes",
note = "PORmapnr. 1812; 12th Congress of the European Association of Agricultural Economists ; Conference date: 26-08-2008 Through 29-08-2008",
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Hardaker, JB, Lien, G, van Asseldonk, MAPM, Richardson, W & Hegrenes, A 2008, 'Risk programming and sparse data: how to get more reliable results' Paper presented at 12th Congress of the European Association of Agricultural Economists, 26/08/08 - 29/08/08, .

Risk programming and sparse data: how to get more reliable results. / Hardaker, J.B.; Lien, G.; van Asseldonk, M.A.P.M.; Richardson, W.; Hegrenes, A.

2008. Paper presented at 12th Congress of the European Association of Agricultural Economists, .

Research output: Contribution to conferenceConference paperAcademic

TY - CONF

T1 - Risk programming and sparse data: how to get more reliable results

AU - Hardaker, J.B.

AU - Lien, G.

AU - van Asseldonk, M.A.P.M.

AU - Richardson, W.

AU - Hegrenes, A.

N1 - PORmapnr. 1812

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

M3 - Conference paper

ER -

Hardaker JB, Lien G, van Asseldonk MAPM, Richardson W, Hegrenes A. Risk programming and sparse data: how to get more reliable results. 2008. Paper presented at 12th Congress of the European Association of Agricultural Economists, .