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

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  • Hardaker, J. Brian
  • Lien, Gudbrand D.
  • Van Asseldonk, Marcel A.P.M.
  • Richardson, James W.
  • Hegrenes, Agnar

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.

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Bibliographic Info

Paper provided by European Association of Agricultural Economists in its series 2008 International Congress, August 26-29, 2008, Ghent, Belgium with number 44051.

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Date of creation: 2008
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Handle: RePEc:ags:eaae08:44051

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Keywords: Risk programming; states of nature; sparse data; Risk and Uncertainty;

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References

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  1. Flaten, O. & Lien, G., 2007. "Stochastic utility-efficient programming of organic dairy farms," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1574-1583, September.
  2. Richardson, James W. & Klose, Steven L. & Gray, Allan W., 2000. "An Applied Procedure For Estimating And Simulating Multivariate Empirical (Mve) Probability Distributions In Farm-Level Risk Assessment And Policy Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(02), August.
  3. David J. Pannell, 2006. "Flat Earth Economics: The Far-reaching Consequences of Flat Payoff Functions in Economic Decision Making," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(4), pages 553-566.
  4. Lence, Sergio H. & Hayes, Dermot J., 1995. "Land Allocation In The Presence Of Estimation Risk," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 20(01), July.
  5. Nanseki, Teruaki & Morooka, Yoshinori, 1991. "Risk preference and optimal crop combinations in upland Java, Indonesia: An application of stochastic programming," Agricultural Economics, Blackwell, vol. 5(1), pages 39-58, January.
  6. G Lien & JB Hardaker, 2001. "Whole-farm planning under uncertainty: impacts of subsidy scheme and utility function on portfolio choice in Norwegian agriculture," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 28(1), pages 17-36, March.
  7. J. Brian Hardaker & Louise H. Patten & David J. Pannell, 1988. "Utility‐Efficient Programming For Whole‐Farm Planning," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 32(2-3), pages 88-97, 08-12.
  8. Pannell, David J. & Nordblom, Thomas L., 1998. "Impacts of risk aversion on whole-farm management in Syria," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 42(3), September.
  9. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
  10. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  11. Torkamani, Javad, 2005. "Using a whole-farm modelling approach to assess prospective technologies under uncertainty," Agricultural Systems, Elsevier, vol. 85(2), pages 138-154, August.
  12. Dorward, Andrew, 1999. "A Risk Programming Approach for Analysing Contractual Choice in the Presence of Transaction Costs," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 26(4), pages 479-92, December.
  13. Lien, Gudbrand D. & Hardaker, J. Brian & Richardson, James W., 2006. "Simulating Multivariate Distributions with Sparse Data: A Kernal Density Smoothing Procedure," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25449, International Association of Agricultural Economists.
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Cited by:
  1. Acs, Szvetlana & Berentsen, Paul & Huirne, Ruud & van Asseldonk, Marcel, 2009. "Effect of yield and price risk on conversion from conventional to organic farming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(3), September.
  2. Kandulu, John, 2011. "Assessing the potential for beneficial diversification in rain-fed agricultural enterprises," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100568, Australian Agricultural and Resource Economics Society.

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