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Forecasting livestock prices: Fixed and stochastic coefficients estimation comparisons
Author(s) -
Conway Roger K.,
Hallahan Charles B.,
Stillman Richard P.,
Prentice Paul T.
Publication year - 1990
Publication title -
agribusiness
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.57
H-Index - 43
eISSN - 1520-6297
pISSN - 0742-4477
DOI - 10.1002/1520-6297(199001)6:1<15::aid-agr2720060103>3.0.co;2-l
Subject(s) - econometrics , economics , livestock , ordinary least squares , commodity , econometric model , estimation , maximum likelihood , sample (material) , mathematics , statistics , geography , finance , chemistry , management , chromatography , forestry
Agricultural commodity analysts have systematically overpredicted livestock prices during the 1980s by using econometric forecasting models that do not account for changing economic conditions. This article compares the out‐of‐sample forecast performance of the Swamy—Tinsley stochastic coefficients model with ordinary least squares, Cochrane—Orcutt, and maximum likelihood procedures that estimate red meat and chicken prices. The ability of a stochastic coefficients model to adapt quickly to changing economic conditions helps make it almost uniformly superior to a fixed coefficients model in forecasting the quarterly retail price for beef and chicken. The Cochrane—Orcutt and maximum‐likelihood procedures appear to forecast pork prices better.