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Price Forecasting With Time-Series Methods And Nonstationary Data: An Application To Monthly U.S. Cattle Prices

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  • Zapata, Hector O.
  • Garcia, Philip

Abstract

The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of nonstationarity. Procedures that permit model specifications consistent with the system's dynamics provide the most accurate forecasts.

Suggested Citation

  • Zapata, Hector O. & Garcia, Philip, 1990. "Price Forecasting With Time-Series Methods And Nonstationary Data: An Application To Monthly U.S. Cattle Prices," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 15(1), pages 1-10, July.
    • Handle: RePEc:ags:wjagec:32505
    DOI: 10.22004/ag.econ.32505
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    References listed on IDEAS

    as
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    5. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    6. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
    7. David A. Bessler & John L. Kling, 1986. "Forecasting Vector Autoregressions with Bayesian Priors," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(1), pages 144-151.
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    Cited by:

    1. Covey, Ted & Bessler, David A., 1991. "The Role of Futures in Daily Forward Pricing," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271282, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. P. Geoffrey Allen & Robert Fildes, 2005. "Levels, Differences and ECMs – Principles for Improved Econometric Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 881-904, December.
    3. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2008. "How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37620, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    4. Florkowski, Wojciech J. & Lai, Yue, 1997. "Cointegration Between Prices of Pecans and Other Edible Nuts: Forecasting and Implications," 1997 Annual Meeting, July 13-16, 1997, Reno\ Sparks, Nevada 35870, Western Agricultural Economics Association.
    5. Elham Rahmani & Mohammad Khatami & Emma Stephens, 2024. "Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada," Sustainability, MDPI, vol. 16(5), pages 1-19, February.

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