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


  • Zapata, Hector O.
  • Garcia, Philip


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 systemÂ’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(01), July.
  • Handle: RePEc:ags:wjagec:32505

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    References listed on IDEAS

    1. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    2. 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.
    3. 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.
    4. Robert B. Litterman, 1979. "Techniques of forecasting using vector autoregressions," Working Papers 115, Federal Reserve Bank of Minneapolis.
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