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Forecast Evaluation For Multivariate Time-Series Models: The U.S. Cattle Market

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  • Park, Timothy A.

Abstract

A set of rigorous diagnostic techniques is used to evaluate the forecasting performance of five multivariate time-series models for the U.S. cattle sector. The root-mean-squared-error criterion along with an evaluation of the rankings of forecast errors reveals that the Bayesian vector autoregression (BVAR) and the unrestricted VAR (UVAR) models generate forecasts which are superior to both a restricted VAR (RVAR) and a vector autoregressive moving-average (VARMA) model. Two methods for calculating a test evaluating the ability to forecast directional changes are implemented. The BVAR models and the UVAR model unambiguously outperform the VARMA model in the forecasting directional change

Suggested Citation

  • Park, Timothy A., 1990. "Forecast Evaluation For Multivariate Time-Series Models: The U.S. Cattle Market," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 15(1), pages 1-11, July.
    • Handle: RePEc:ags:wjagec:32495
    DOI: 10.22004/ag.econ.32495
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    References listed on IDEAS

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    3. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    4. 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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    1. Robledo, Carlos W. & Zapata, Hector O. & McCracken, Michael, 2001. "New Mse Tests For Evaluating Forecasting Performance: Empirics And Bootstrap," 2001 Annual meeting, August 5-8, Chicago, IL 20686, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Aubry, Mathilde & Renou-Maissant, Patricia, 2014. "Semiconductor industry cycles: Explanatory factors and forecasting," Economic Modelling, Elsevier, vol. 39(C), pages 221-231.
    3. 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.
    4. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
    5. 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.
    6. McCracken, Michael W., 2004. "Parameter estimation and tests of equal forecast accuracy between non-nested models," International Journal of Forecasting, Elsevier, vol. 20(3), pages 503-514.
    7. Obalade Adefemi Alamu & Ebiwonjumi Ayooluwade & Adaramola Anthony Olugbenga, 2019. "Var Modelling of Dynamics of Poverty, Unemployment, Literacy and Per Capita Income in Nigeria," Folia Oeconomica Stetinensia, Sciendo, vol. 19(1), pages 73-88, June.
    8. Kuhns, Annemarie & Leibtag, Ephraim & Volpe, Richard & Roeger, Ed, 2015. "How USDA Forecasts Retail Food Price Inflation," Technical Bulletins 206500, United States Department of Agriculture, Economic Research Service.

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    Livestock Production/Industries;

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