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Price Density Forecasts in the U.S. Hog Markets: Composite Procedures
Author(s) -
TrujilloBarrera Andres,
Garcia Philip,
Mallory Mindy L.
Publication year - 2016
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1093/ajae/aaw050
Subject(s) - weighting , econometrics , logarithm , composite index , statistics , value (mathematics) , calibration , composite number , goodness of fit , sample (material) , mathematics , consensus forecast , probability density function , economics , thermodynamics , algorithm , composite indicator , mathematical analysis , physics , medicine , radiology
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.