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Statistical Significance and Stability of the Hog Cycle

Published online by Cambridge University Press:  05 September 2016

J. Scott Shonkwiler  and
Thomas H. Spreen
J. Scott Shonkwiler
Affiliation:
Department of Food and Resource Economics, University of Florida
Thomas H. Spreen
Affiliation:
Department of Food and Resource Economics, University of Florida
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Abstract

Cyclical fluctuations in prices and production have long characterized the United States hog industry. Recent evidence suggests that the length of the hog cycle has changed. In order to determine whether the change in cycle length is statistically significant, the bootstrap technique is employed to derive confidence intervals for point estimates of the hog cycle. Application of the bootstrap technique to time series models is discussed and empirical results are presented. It is concluded that the hog cycle is undergoing rather complicated changes based on cycle lengths that are calculated to be statistically different from zero.

Keywords

hog markettime seriesboot-strappingcycleautoregressive modelstructural change
Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 18 , Issue 2 , December 1986 , pp. 227 - 234
Copyright
Copyright © Southern Agricultural Economics Association 1986

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References

Box, G. E.P. and Jenkins, G. M.. Times Series Analysis. San Francisco: Holden-Day, 1976.Google Scholar
Breimyer, H. F. “Emerging Phenomenon: A Cycle in Hogs.” J. Farm Econ. 41(1959):760–768.CrossRefGoogle Scholar
Brock, W. A. “On Models of Expectations That Arise From Maximizing Behavior of Economic Agents Over Time.” J. Econ. Theory, 5(1972):348–376.CrossRefGoogle Scholar
Chow, G. C. Analysis and Control of Dynamic Economic Systems. New York: Wiley, 1975.Google Scholar
Day, R. H. “Irregular Growth Cycles.” Amer. Econ. Rev., 72(1982):406–414.Google Scholar
Dean, G. W. and Heady, E. O.. “Changes in Supply Response and Elasticity for Hogs.” J. Farm Econ., 41(1959):845–860.Google Scholar
Dixon, B. L. and Martin, L. J.. “Forecasting U.S. Pork Production Using a Random Coefficient Model.” Amer. J. Agr. Econ., 64,3(1982):530–538.CrossRefGoogle Scholar
Effron, B. “Bootstrap Methods: Another Look at the Jackknife.” Annals of Stat., 7(1979):1–26.Google Scholar
Effron, B. and Gong, G.. “A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation.” Amer. Stat., 37(1983):36–48.Google Scholar
Freedman, D. A. “Bootstrapping Regression Models.” Annals of Stat., 9(1981):1,218–1,228.CrossRefGoogle Scholar
Freedman, D. A. and Peters, S. C.. “Bootstrapping a Regression Equation: Some Empirical Results.” J. Amer. Stat. Assn., 79(1984):97–106.CrossRefGoogle Scholar
Fuller, W. A. Introduction to Statistical Time Series. New York: John Wiley and Sons, 1976.Google Scholar
Gordon, D. and Hines, A.. “On the Theory of Price Dynamics.” in Phelps, E. S. ed. Microeconomic Foundations of Employment and Inflation Theory. W. W. Norton, 1970.Google Scholar
Grandmont, J. “On Endogenous Competitive Business Cycles.” Econometrica, 53(1985):995–1,045.CrossRefGoogle Scholar
Granger, C. W.J. and Newbold, P.. Forecasting Economic Times Series. New York: Academic Press, 1977.Google Scholar
Greenberg, E. and Webster, C. E.. Advanced Econometrics: A Bridge to the Literature. New York: Wiley, 1983.Google Scholar
Harlow, A. A. “The Hog Cycle and the Cobweb Theorem.” J. Farm Econ., 42(1960):842–853.Google Scholar
Hayenga, M., Rhodes, V. J., Brandt, J. A., and Deiter, R. E.. The U.S. Pork Sector: Changing Structure and Organization. Iowa State University Press, 1985.Google Scholar
Jelavich, M. S. “Distributed Lag Estimation of Harmonic Motion in the Hog Market.” Amer. J. Agr. Econ., 55,2(1973):223–224.CrossRefGoogle Scholar
Judge, G. G., Griffiths, W. E., Hill, R. C., and Lee, T. C.. The Theory and Practice of Econometrics. New York: Wiley, 1980.Google Scholar
May, R. M. “Simple Mathematical Models with Very Complicated Dynamics.” Nature, 261 (June, 1976):459–467.CrossRefGoogle ScholarPubMed
Nelson, C. R. Applied Times Series Analysis for Managerial Forecasting San Francisco: Holden-Day, 1973.Google Scholar
Neudecker, H. and van de Panne, C.. “Asymptotic Standard Errors of Latent Roots of Econometric Systems.” Rev. Inter. Stat. Inst., 34(1966):43–47.CrossRefGoogle Scholar
Plain, R. L. and Williams, J. E.. “Adaptive Planning and Price Uncertainty in Pork Production.” So.J. Agr. Econ., 13,1(1981):39–46.Google Scholar
Shonkwiler, J. S. and Spreen, T. H.. “A Dynamic Regression Model of the U.S. Hog Market.” Can.J. Agr. Econ., 30(1982):37–48.CrossRefGoogle Scholar
Spreen, T. H. and Shonkwiler, J. S.. “The Hog Cycle Revisited.” Paper presented at the meetings of the Amer. Agri. Econ. Assn., Clemson, South Carolina, 1981.Google Scholar
Stillman, R. P. “Hog Operations Becoming Fewer, Larger and More Efficient.” Livestock and Poultry Outlook and Situation Report. USDA, ERS; December, 1984: pp. 52–56.Google Scholar
Talpaz, H. “Multi-Frequency Cobweb Model: Decomposition of the Hog Cycle.” Amer. J. Agr. Econ., 56,1(1974):38–49.CrossRefGoogle Scholar
Theil, H. and Boot, J. C. G.. “The Final Form of Econometric Equation Systems.” Rev. Inter. Stat. Inst., 30(1962):136–152.CrossRefGoogle Scholar
Wallis, K. F. “Multiple Time Series Analysis and the Final Form of Econometric Models.” Econometrica, 45(1977):1,481–1,497.CrossRefGoogle Scholar