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Forecasting U.S. Pork Production Using a Random Coefficient Model
Author(s) -
Dixon Bruce L.,
Martin Larry J.
Publication year - 1982
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 - 10.2307/1240645
Subject(s) - econometrics , production (economics) , term (time) , linear regression , function (biology) , coefficient of determination , correlation coefficient , mathematics , random effects model , regression , statistics , regression analysis , economics , physics , microeconomics , medicine , meta analysis , quantum mechanics , evolutionary biology , biology
A random coefficient regression model is found to be superior to a fixed coefficient model for short‐ and intermediate‐term forecasting of quarterly U.S. pork production. The random coefficient model portrays some regression parameters as the sum of a systematically changing component and random error. Use of such models is discussed. Pork supply is hypothesized as a function of seasonal shifters with geometric lags on hog and feed prices. Results show seasonal effects declining, feed price not being a significant explanatory variable, and pork production adjusting faster to lagged price conditions than indicated by the constant coefficient model.