Price Density Forecasts in the U.S. Hog Markets: Composite Procedures

16 Pages Posted: 16 Apr 2020

See all articles by Andrés Trujillo-Barrera

Andrés Trujillo-Barrera

Wageningen UR

Philip Garcia

University of Illinois at Urbana-Champaign - Department of Agricultural and Consumer Economics

Carl Gaigné

affiliation not provided to SSRN

Date Written: October 2016

Abstract

We develop and evaluate quarterly out‐of‐sample individual and composite density forecasts for U.S. hog prices. Individual density forecasts are generated using time series models and the implied distributions of USDA and Iowa State University outlook forecasts. Composite density forecasts are constructed using linear and logarithmic combinations of the individual forecasts and several weighting schemes. Density forecasts are evaluated on predictive accuracy (sharpness), goodness of fit (calibration), and their economic value in a hedging simulation. Logarithmic combinations using equal and mean square error weights outperform all individual density forecasts and are modestly better than linear composites. Comparison of the outlook forecasts to the best composite demonstrates the usefulness of the composite procedure, and identifies the economic value that more accurate expected price probability distributions can provide to producers.

Keywords: Commodity price analysis, density forecast combination

Suggested Citation

Trujillo-Barrera, Andrés and Garcia, Philip and Gaigné, Carl, Price Density Forecasts in the U.S. Hog Markets: Composite Procedures (October 2016). American Journal of Agricultural Economics, Vol. 98, Issue 5, pp. 1529-1544, 2016, Available at SSRN: https://ssrn.com/abstract=3576996 or http://dx.doi.org/10.1093/ajae/aaw050

Andrés Trujillo-Barrera

Wageningen UR ( email )

Hollandseweg 1
Wageningen, 6706KN
Netherlands

Philip Garcia

University of Illinois at Urbana-Champaign - Department of Agricultural and Consumer Economics ( email )

1301 W. Gregory Drive
427 Mumford Hall
Urbana, IL 61801
United States
217-333-0644 (Phone)
217-333-5538 (Fax)

Carl Gaigné (Contact Author)

affiliation not provided to SSRN

No Address Available

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