Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T00:36:23.205Z Has data issue: false hasContentIssue false
  • English
  • Français

Measuring Historical Risk in Quarterly Milk Prices

Published online by Cambridge University Press:  15 September 2016

Beth Pride Ford ,
Wesley N. Musser  and
Robert D. Yonkers
Beth Pride Ford
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania state University. Address: Armsby Building, University Park, Pennsylvania, 16802
Wesley N. Musser
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania state University. Address: Armsby Building, University Park, Pennsylvania, 16802
Robert D. Yonkers
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania state University. Address: Armsby Building, University Park, Pennsylvania, 16802
Get access Rights & Permissions [Opens in a new window]

Abstract

Various methods have been used to estimate risk indices with historical data. An industry perception of increasing milk price risk over time provides a standard for evaluating several techniques used to measure historical risk. Risk measures from a regression model and an ARIMA model were consistent with the perception of increasing risk.

Type
Articles
Information
Agricultural and Resource Economics Review , Volume 22 , Issue 1 , April 1993 , pp. 20 - 26
Copyright
Copyright © 1993 Northeastern Agricultural and Resource Economics Association 

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, R.M., Menkhaus, D.J., and Woolery, B.A. “Alternative Parameter Specification in EV Analysis: Implications for Farm Level Decision Making.” Western Journal of Agricultural Economics, 5 (1980): 13–20.Google Scholar
Berck, P. “Portfolio Theory and the Demand For Futures: The Case of California Cotton.” American Journal of Agricultural Economics, 63 (1981): 466–74.Google Scholar
Bessler, D.A. “Aggregated Personalistic Beliefs on Yields of Selected Crops Estimated Using ARIMA Processes.” American Journal of Agricultural Economics, 62 (1977): 666–74.Google Scholar
Carter, H.O., and Dean, G.W. “Income, Price, and Yield Variability for Principal California Crops and Cropping Systems.” Hilgardia, 30 (1960): 175–218.Google Scholar
Collins, A., Musser, W.N., and Mason, R. “Prospect Theory and Risk Preferences of Oregon Seed Producers.” American Journal of Agricultural Economics, 73 (1991): 429–35.Google Scholar
Fackler, P.L. “Modeling Trend and Higher Moment Properties of U.S. Corn Yields.” In Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk. Proceedings of the 1989 meetings of Regional Project S-232. Department of Agricultural Economics, Texas A&M University, 1989.Google Scholar
Fackler, P.L., and Young, D.L. “Notes on Modeling Regional Crop Yields.” In Quantifying Long Run Agricultural Risks and Evaluating Farmer Responses to Risk. Proceedings of the 1991 meetings of Regional Project S-232. Department of Agricultural Economics and Rural Sociology, Univ. of Arkansas, 1991.Google Scholar
Fraher, J.S. Reaction to Dairy Situation and Outlook. USDA Annual Agricultural Outlook Conference, Session #C5, November 1990.Google Scholar
Franzmann, J. “Cattle Cycles Revisited.” Southern Journal of Agricultural Economics, 3 (1971): 69–76.Google Scholar
Granger, C.W.J., and Newbold, P. Forecasting Economic Time Series. London: Academic Press, Inc., 1977.Google Scholar
Hamm, L.G. The Dairy Industry in the 1990's. USDA Annual Agricultural Outlook Conference, Session #C5, November, 1990.Google Scholar
Hammida, M. and Eidman, V. “Livestock and Poultry Production Risk in the United States,” Department of Agricultural and Applied Economics, Staff Paper No. P91–21, University of Minnesota, June 1991.Google Scholar
Harvey, A.C. Time Series Models. New York: John Wiley and Sons, Inc., 1981.Google Scholar
Johnston, J. Econometric Methods. New York: McGraw-Hill, Inc., 1984.Google Scholar
Judge, G.G., Hill, R.C., Griffiths, W.E., Lutkepohl, H., and Lee, T.C. Introduction to the Theory and Practice of Econometrics. New York: John Wiley and Sons, Inc., 1982.Google Scholar
Kmenta, J. Elements of Econometrics, 2nd edition. New York: McMillan Publishing Co., 1986.Google Scholar
Kramer, R.A., McSweeny, W.T., and Stavros, R. “Soil Conservation With Uncertainty.” American Journal of Agricultural Economics, 65 (1983): 694–702.CrossRefGoogle Scholar
Marra, M.C., and Carlson, G.A. “The Decision to Double Crop: An Application of Expected Utility Theory Using Stein's Theorem.” American Journal of Agricultural Economics, 72 (1990): 337–45.Google Scholar
McSweeny, W.T., Kenyon, D.E., and Kramer, R.A. “Uncertainty in Risk Programming.” American Journal of Agricultural Economics, 69 (1987): 87–96.Google Scholar
Musser, W.N., Mapp, H.P. Jr., and Barry, P.J. “Applications 1: Risk Programming.” In Risk Management in Agriculture. ed. Barry, P.J. Ames, IA: The Iowa State University Press, 1984.Google Scholar
Musser, W.N., and Stamoulis, G. “Evaluating the Food and Agricultural Act of 1977 With Firm Quadratic Risk Programming.” American Journal of Agricultural Economics, 63 (1981): 447–56.Google Scholar
Pankratz, A. Forecasting With Univariate Box-Jenkins Models: Concepts and Cases. New York: John Wiley and Sons, Inc., 1983.Google Scholar
Persaud, T., and Mapp, H.P. “Analysis of Alternative Production and Marketing Strategies in Southwestern Oklahoma: A MOTAD Approach.” In Risk Analysis in Agriculture: Research and Educational Developments. Department of Agricultural Economics, University of Illinois, AE-4492, 1980.Google Scholar
Swinton, S.M., and King, R.P. “Evaluating Robust Regression Techniques for Detrending Crop Yield Data with Nonnormal Errors.” American Journal of Agricultural Economics 73 (1991): 446–51.Google Scholar
Tintner, G. The Variate Difference Method. Bloomington, IN: Principia Press, 1940.Google Scholar
U.S. Department of Agriculture, Economic Research Service. Dairy: Background for 1990 Farm Legislation. Staff Report AGES 9020, March 1990.Google Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service. Agricultural Prices: Annual Summary. Various issues.Google Scholar
White, F.C., and Musser, W.N. “Inflation Effects in Farm Financial Management.” American Journal of Agricultural Economics, 67 (1980): 1060–64.Google Scholar
Young, D.L. “Evaluating Procedures For Computing Objective Risk From Historical Time Series.” In Risk Analysis in Agriculture: Research and Educational Developments. Department of Agricultural Economics, University of Illinois, AE-4492, 1980.Google Scholar
Young, D.L. “Risk Concepts and Measures For Decision Analysis.” In Risk Management in Agriculture. ed. Barry, P.J. Ames, IA: The Iowa State University Press, 1984.Google Scholar