Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-25T05:19:04.327Z Has data issue: false hasContentIssue false
  • English
  • Français

An Applied Procedure for Estimating and Simulating Multivariate Empirical (MVE) Probability Distributions In Farm-Level Risk Assessment and Policy Analysis

Published online by Cambridge University Press:  28 April 2015

James W. Richardson ,
Steven L. Klose  and
Allan W. Gray
James W. Richardson
Affiliation:
Texas A&M University
Steven L. Klose
Affiliation:
Texas A&M University
Allan W. Gray
Affiliation:
Purdue University
Get access Rights & Permissions [Opens in a new window]

Extract

Simulation as an analytical tool continues to gain popularity in industry, government, and academics. For agricultural economists, the popularity is driven by an increased interest in risk management tools and decision aids on the part of farmers, agribusinesses, and policy makers. Much of the recent interest in risk analysis in agriculture comes from changes in the farm program that ushered in an era of increased uncertainty. With increased planting flexibility and an abundance of insurance and marketing alternatives farmers face the daunting task of sorting out many options in managing the increased risk they face. Like farmers, decision makers throughout the food and fiber industry are seeking ways to understand and manage the increasingly uncertain environment in which they operate. The unique abilities of simulation as a tool in evaluating and presenting risky alternatives together with an expected increase in commodity price risk, as projected by Ray, et al., will likely accelerate the interest in simulation for years to come.

Type
Invited Paper Sessions
Information
Journal of Agricultural and Applied Economics , Volume 32 , Issue 2 , August 2000 , pp. 299 - 315
Copyright
Copyright © Southern Agricultural Economics Association 2000

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, G. Personal Communication Regarding FAPRI Crop Model Development. Food and Agricultural Policy Research Institute, University of Missouri-Columbia, November 1999.Google Scholar
AFPC website, http://afpcl.tamu.edu/. Agricultural and Food Policy Center, Department of Agricultural Economics, Texas A&M University, 1999.Google Scholar
Agrawal, R.C. and Heady, E.O.. Operation Research Methods for Agricultural Decisions. Ames: The Iowa State University Press, 1972.Google Scholar
Anderson, J.R., Dillan, J.L. and Hardaker, J.B.. Agricultural Decision Analysis. Ames: The Iowa State University Press, 1977.Google Scholar
Clements, A.M. Jr., Mapp, H.P. Jr., and Eidman, V.R.. A Procedure for Correlating Events in Farm Firm Simulation Models. Technical Bulletin T-131, Oklahoma Agricultural Experiment Station, August 1971.Google Scholar
Decisioneering. 1996. Crystal Ball, Forecasting & Risk Analysis for Spreadsheet Users. Denver, CO. Decisioneering.Google Scholar
Eidman, V.R. (editor). Agricultural Production Systems Simulation. Proceedings of a workshop by the Southern Farm Management Research Committee. Stillwater: Oklahoma State University, May 1971.Google Scholar
Fackler, P.L. “Modeling Interdependence: An Approach to Simulation and Elication.” American Journal of Agricultural Economics 73(1991):1091–1097.CrossRefGoogle Scholar
Food and Agricultural Policy Research Institute (FAPRI). November 1999 Baseline. University of Missouri-Columbia, November 1999.Google Scholar
Gray, A.W.Agribusiness Strategic Planning Under Risk. Department of Agricultural Economics, Texas A&M University, Ph.D. Dissertation, August 1998.Google Scholar
Guide to ©RISK: Risk Analysis and Simulation Add-In for Microsoft Excel or Lotus 1-2-3. Newfield: Palisade Corp. 1996.Google Scholar
Hardaker, J.B., Huirne, R.B.M., and Anderson, J.R.. Coping With Risk in Agriculture. New York: CAB International, 1997.Google Scholar
King, R.P.Operational Techniques for Applied Decision Analysis Under Uncertainty. Ph.D. dissertation, Department of Agricultural Economics, Michigan State University, 1979.Google Scholar
King, R.P, Black, J.R., Benson, F.J., and Pav-kov, P.A.. “The Agricultural Risk Management Simulator Microcomputer Program.” Southern Journal of Agricultural Economics 20(Dec. 1988):165–171.Google Scholar
Klose, S.L. and Outlaw, J.L.. FARM Assistance: Technical Description of the Financial and Risk Management Assistance Model. Department of Agricultural Economics, Texas Agricultural Extension Service, forthcoming.Google Scholar
Law, A.M. and Kelton, W.D.. Simulation Modeling and Analysis. 2nd edition, New York: McGraw-Hill Book Co., 1991.Google Scholar
Li, S.T. and Hammond, J.L.. “Generation of Pseudorandom Numbers with Specified Univariate Distributions and Correlation Coefficients.” IEEE Transactions on Systems, Management and Cybernetics Sept. 1975:557–561.CrossRefGoogle Scholar
Ray, Daryli E., Richardson, James W., De La Torre Ugarte, Daniel G., and Tiller, Kelly H.. “Estimating Price Variability in Agriculture: Implications for Decision Makers.” Journal of Agricultural and Applied Economics, 30,1 (July 1998):21–33.CrossRefGoogle Scholar
Richardson, J.W. and Nixon, C.J.. UAP-Agribusi-ness Financial Analyzer. College Station, Texas, 1999a.Google Scholar
Richardson, J.W. and Nixon, C.J.. UAP-Farm Financial Analyzer. College Station, Texas, 1999b.Google Scholar
Richardson, J.W.Simulation: A Tool for Decision Making Under Risk. Department of Agricultural Economics and Agricultural and Food Policy Center, Texas A&M University (Mimeo), June 1999.Google Scholar
Richardson, J.W. and Nixon, C.J.. Description of FLIPSIM V: A General Firm Level Policy Simulation Model. Bulletin 1528, Texas Agricultural Experiment Station, July 1986.Google Scholar
Richardson, J.W. and Condra, G.D.. “Farm Size Evaluation in the El Paso Valley: A Survival/Success Approach.” American Journal of Agricultural Economics, 63(1981):430–437.CrossRefGoogle Scholar
Richardson, J.W. and Condra, G.D.. A General Procedure for Correlating Events in Simulation Models. Department of Agricultural Economics, Texas A&M University, Mimeo, May 1978. Savage, S.L. Insight. XLA: Business Analysis Software for Microsoft Excel. New York: Duxbury Press, 1998.Google Scholar
Taylor, C.R. “Two Practical Procedures for Estimating Multivariate Nonnormal Probability Density Functions.” American Journal of Agricultural Economics 72(1990):210–17.CrossRefGoogle Scholar
TRMEP website, http://trmep.tamu.edu/. Texas Agricultural Extension Service, Texas A&M University, 1999.Google Scholar
Van Tassell, L.W., Richardson, J.W. and Conner, J.R.. Empirical Distributions and Production Analysis: A Documentation Using Meteorological Data. Bulletin 671, The University of Tennessee Agricultural Experiment Station, September 1989.Google Scholar
Winston, W.L.Simulation Modeling Using ©Risk. New York: Duxbury Press, 1996.Google Scholar