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A Comparison of Nominal and Real Historical Risk Measures

Published online by Cambridge University Press:  28 April 2015

Beth Pride Ford  and
Wesley N. Musser
Beth Pride Ford
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University
Wesley N. Musser
Affiliation:
University of Maryland and former professor of Agricultural Economics, The Pennsylvania State University
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Abstract

Previous studies of historical risk have used either nominal or real data to calculate risk measures for agricultural prices and income. However, the effects of using nominal and real data have not been evaluated. This study utilizes theoretical variance approximation relationships to examine variances from detrended real and nominal time series. The relationships between variances are derived for quarterly U.S. farm milk prices for 1960-72, 1973-80, and 1981-90. Contrary to common intuitive arguments, results indicate that variances of real time series can be larger than variances of nominal series. While definitive conclusions are not possible, several reasons for using nominal data in risk analysis are given.

Keywords

detrendingindicesnominal datarisk measurement
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 27 , Issue 2 , December 1995 , pp. 669 - 685
Copyright
Copyright © Southern Agricultural Economics Association 1995

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References

Adams, R.M., Menkhaus, D.J., and Woolery, B.A.. “Alternative Parameter Specification in EV Analysis: Implications for Farm Level Decision Making.” West. J. Agr. Econ., 5(1980): 13–20.Google Scholar
Bohrnstedt, G.W., and Goldberger, A.S.. “On the Exact Covariance of Products of Random Variables.” J. Amer. Stat. Assoc. 64(1969): 1439-42.CrossRefGoogle Scholar
Brake, J.R. “Inflation and Monetary Risks for Agricultural Producers.” In Risk Management in Agriculture. Ed. P. J. Barry. Ames, IA: The Iowa State University Press, 1984.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.CrossRefGoogle Scholar
Coyle, B. “Price Risk and Duality.” Amer. J. Agr. Econ. 74(1992):849-59.CrossRefGoogle 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. San Antonio, Texas, March 1991.Google Scholar
Ford, B.P., Musser, W.N., and Yonkers, R.D.. “Measuring Historical Risk in Quarterly Milk Prices.” Agr. Res. Econ. Rev., 22(1993):20–25.Google Scholar
Hazell, P.B.R. “A Linear Alternative to Quadratic and Semivariance Programming for Farm Planning Under Uncertainty.” Amer. J. Agr. Econ., 53(1971):53–62.CrossRefGoogle Scholar
Johnston, J.Econometric Methods. New York: McGraw-Hill, Inc., 1984.Google Scholar
Judge, G.G., Griffiths, W.E., Hill, R.C., and Lee, T.C.. The Theory and Practice of Econometrics. New York: John Wiley and Sons, Inc., 1980.Google Scholar
Kramer, R.A., McSweeney, W.T., and Stavros, R.. “Soil Conservation With Uncertainty.” Amer. J. Agr. Econ., 65(1983):694–702.CrossRefGoogle Scholar
Levy, H., and Sarnat, M.. Investment and Portfolio Analysis. New York: John Wiley and Sons, Inc., 1972.Google Scholar
Mapp, H.P. Jr., and Helmers, G.A.. “Methods of Risk Analysis for Farm Firms.” In Risk Management in Agriculture. Ed. P. J. Barry. Ames, IA: The Iowa State University Press, 1984.Google Scholar
Mathia, G. A. “Empirical Measures of Risk for Selected Field and Horticultural Crops.” So. J. Agr. Econ., 8(1976): 115-22.Google Scholar
Mood, A.M., Graybill, F.A., and Boes, D.C.. Introduction to the Theory of Statistics. New York: McGraw-Hill, Inc., 1974.Google Scholar
Miranda, M. J. “Area-Yield Crop Insurance.” Amer. J. Agr. Econ., 73(1991):233-42.CrossRefGoogle Scholar
Miranda, M.J., and Glauber, J.W.. “Providing Crop Disaster Assistance Through a Modified Deficiency Payment Program.” Amer. J. Agr. Econ., 73(1991): 1233-43.CrossRefGoogle Scholar
Paris, Q. “Comparative Statics Under Uncertainty.” Amer. J. Agr. Econ., 70(1988):133-41.CrossRefGoogle Scholar
Pope, R. D. “Expected Utility Hypothesis.” Amer. J. Agr. Econ., 60(1978):619-27.CrossRefGoogle Scholar
Swinton, S.M., and King, R.P.. “Evaluating Robust Regression Techniques for Detrending Crop Yield Data with Nonnormal Errors.” Amer. J. Agr. Econ., 73(1991):446-51.CrossRefGoogle Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service. Agricultural Prices: Annual Summary. Various issues.Google Scholar
U.S. Department of Agriculture, Economic Research Service. Costs of Producing Milk. Various issues.Google Scholar
Walker, M.E. Jr., and T.K., Lin. “Price, Yield, and Gross Revenue Variability for Selected Georgia Crops.” So. J. Agr. Econ., 10(1978):71–75.Google Scholar
White, F.C, and N.W., Musser “Inflation Effects on Farm Financial Management.” Amer. J. Agr. Econ., 62(1980): 1060-64.CrossRefGoogle Scholar
Young, D.L. “Evaluating Procedures For Computing Objective Risk From Historical Time Series.” In Risk Analysis in Agriculture: Research and Educational Developments. Dept. of Agr. Econ., Univ. of III., AE-4492, 1980.Google Scholar
Young, D.L. “Risk Concepts and Measures For Decision Analysis.” In Risk Management in Agriculture. Ed. P. J. Barry. Ames, IA: The Iowa State University Press, 1984.Google Scholar