Forecast Evaluation For Multivariate Time-Series Models: The U.S. Cattle Market
AbstractA 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
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Bibliographic InfoArticle provided by Western Agricultural Economics Association in its journal Western Journal of Agricultural Economics.
Volume (Year): 15 (1990)
Issue (Month): 01 (July)
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- 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).
- 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.
- 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.
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