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

  • Park, Timothy A.

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

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File URL: http://purl.umn.edu/32495
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Article provided by Western Agricultural Economics Association in its journal Western Journal of Agricultural Economics.

Volume (Year): 15 (1990)
Issue (Month): 01 (July)
Pages:

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Handle: RePEc:ags:wjagec:32495
Contact details of provider: Web page: http://waeaonline.org/

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  1. Stillman, Richard P., 1985. "A Quarterly Model of the Livestock Industry," Technical Bulletins 157008, United States Department of Agriculture, Economic Research Service.
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