Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-28T02:59:50.240Z Has data issue: false hasContentIssue false
  • English
  • Français

The Effects of Firm Size and Production Cost Levels on Dynamically Optimal After-Tax Cotton Storage and Hedging Decisions

Published online by Cambridge University Press:  08 February 2017

Russell Tronstad
Russell Tronstad*
Affiliation:
University of Arizona
Get access Rights & Permissions [Opens in a new window]

Abstract

Farm size and production costs are varied in a six state variable stochastic dynamic programming model that quantifies monthly hedging, storage, and cash cotton sale decisions for an Alabama cotton producer. State variables considered are: (1) cash cotton price; (2) basis level; (3) before-tax income level; (4) cotton holdings; (5) futures position; and (6) value of futures position. Results indicate that when farm size and production cost level differ, marketing decisions diverge the most for cash cotton sales at the end of the tax year and lower range of cash price (less than $.65/lb.), basis (less than -$.05/lb.), and before-tax income (less than $0.00) states.

Keywords

farm sizeproduction costsstochastic dynamic programming
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 23 , Issue 1 , July 1991 , pp. 165 - 179
Copyright
Copyright © Southern Agricultural Economics Association 1991

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

Antle, J.M. “Incorporating Risk in Production Analysis.” Am. J. Agr. Econ., 62(1982): 89–97.Google Scholar
Bera, A.K., and Jarque, C. M.. “An Efficient Large-Sample Test for Normality of Observations and Regression Residuals.” Australian National University, Working Paper No. 040, March 1981.Google Scholar
Berk, P. ‘Tortfolio Theory and Demand for Futures: The Case of California Cotton.” Am. J. Agr. Econ., 63(1981):466–474.CrossRefGoogle Scholar
Brandt, J.A., and Bessler, D.A.. “Composite Forecasting: An Application with U.S. Hog Prices.” Am. J. Agr. Econ., 63(1981): 135–140.Google Scholar
Brown, S.L. “A Reformulation of the Portfolio Model of Hedging.” Am. J. Agr. Econ., 67( 1985): 508–512.CrossRefGoogle Scholar
Burt, O.R. “Dynamic Programming: Has Its Day Arrived?” West J. Agr. Econ., 7(1982): 381–394.Google Scholar
Burt, O.R., and Taylor, C.R.. “Reduction of State Variable Dimensions in Stochastic Dynamic Optimization Models Which Use Tune Series Data.” West J. Agr. Econ., 14(1989): 213–222.Google Scholar
Harris, K.S., and Leuthold, R.M.. “A Comparison of Alternative Forecasting Techniques for Livestock Prices: A Case Study.” N. Cent. J. Agr. Econ., 7(1985): 40–50.Google Scholar
Helmers, G.A., and Held, L.F.. “Comparison of Livestock Price Forecasting Using Simple Techniques, Forward Pricing and Outlook Information.” West J. Agr. Econ., 2(1977): 157–160.Google Scholar
Howard, R.A. Dynamic Programming and Markov Processes. The Massachusetts Institute of Technology: Cambridge, MA, 1960.Google Scholar
Just, R.E. “Risk Aversion under Profit Maximization.” Am. J. Agr. Econ., 57(1975): 347–352.Google Scholar
Kahl, K.H. “Determination of the Recommended Hedging Ratio.” Am. J. Agr. Econ., 65(1983): 603–605.Google Scholar
Karp, L. “Methods for Selecting the Optimal Dynamic Hedge When Production is Stochastic.” Am. J. Agr. Econ,, 69(1987): 647–657.Google Scholar
Kreps, D.M., and Porteus, E.L.. ‘Temporal Resolution of Uncertainty and Dynamic Choice Theory.” Econometrica, 46(1978): 185–200.Google Scholar
Martin, N.R. Professor of Agricultural Economics and Rural Sociology, Auburn University. Personal Communication.Google Scholar
McIntosh, C.S., and Bessler, D.A.. “Forecasting Agricultural Prices Using a Bayesian Composite Approach.” So. J. Agr. Econ., 20(1988): 73–80.Google Scholar
Mims, A.M. “Government Farm Program Participation under Selected Policy Provisions for Typical Alabama Cotton Farms.” Masters Thesis, Auburn University, December 1987.Google Scholar
Mossin, J. “A Note on Uncertainty and Preferences in a Temporal Context.” Am. Econ. Rev., 59(1969): 172–174.Google Scholar
Pope, R.D. “Empirical Estimation and Use of Risk Preferences: An Appraisal of Estimation Methods that Use Actual Economic Decisions.” Am. J. Agr. Econ., 64(1982): 376–383.Google Scholar
Spriggs, J. “Forecasts of Indiana Monthly Farm Prices Using Univariate Box-Jenkins Analysis and Corn Futures Prices.” N. Cent. J. Agr. Econ., 3(1981): 81–87.Google Scholar
Taylor, C.R. “Risk Aversion versus Expected Profit Maximization.” Am. J. Agr. Econ., 68(1986): 137–143.Google Scholar
Tronstad, R., and Taylor, C.R.. “Dynamically Optimal After-Tax Grain Storage, Cash Grain Sale, and Hedging Strategies.” Am. J. Agr. Econ., 73(1991): in press.Google Scholar
Tronstad, R., and Taylor, C.R.. “Effects of the 1986 Tax Reform Act on Grain Marketing Decisions: A Case Study of Winter Wheat Producers.” Cent. J. Agr. Econ., 11(1989): 309–320.Google Scholar