Improving the accuracy of outlook price forecasts

Authors

  • Evelyn V. Colino,

    Corresponding author
    1. Escuela de Economía, Administración y Turismo, Universidad Nacional de Río Negro, Villegas 147, San Carlos de Bariloche 8400, Río Negro, Argentina
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  • Scott H. Irwin,

    1. Department of Agricultural and Consumer Economics, 344 Mumford Hall, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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  • Philip Garcia

    1. Department of Agricultural and Consumer Economics, 344 Mumford Hall, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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  • Data Appendix Available Online
    A data appendix to replicate main results is available in the online version of this article. Please note: Wiley-Blackwell, Inc. is not responsible for the content or functionality of any supporting information supplied by the author. Any queries (other than missing material) should be directed to the corresponding author for the article.

Corresponding author. Tel.: +54 2944 431602; fax: +54 2944 431988.
E-mail address: Evelyn.dv.colino@gmail.com (E.V. Colino).

Abstract

This study investigates the predictive ability of outlook hog price forecasts released by Iowa State University relative to alternative time-series and market forecasts. Under root mean squared error (RMSE), the futures market forecast is most accurate at the first and second horizon but less accurate than Iowa outlook and the other forecast methods at the third horizon. In terms of the individual time-series models, some vector autoregressions (VARs) and Bayesian VARs flexible in specification and estimation and model averaging tend to perform better than Iowa outlook forecasts. Evidence from encompassing tests, more stringent tests of forecast performance, indicates that many price forecasts can add incremental information to the Iowa forecast. Simple combinations of these models and outlook forecasts are able to reduce forecast errors by economically significant levels. Overall, the results indicate that it is possible to provide more accurate forecasts than Iowa outlook at every horizon.

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