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Conditional Qualitative Forecasting
Author(s) -
Feather Peter M.,
Kaylen Michael S.
Publication year - 1989
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 - 10.2307/1241788
Subject(s) - econometrics , bayesian probability , autoregressive integrated moving average , computer science , sample (material) , statistics , artificial intelligence , economics , machine learning , mathematics , time series , chemistry , chromatography
A bayesian method for conditioning the forecast of a qualitative variable on a vector of other qualitative variables is presented. An application is made to forecast the direction of quarterly hog price movements using the direction forecasts from an econometric model, an ARIMA model, and an expert as the conditioning vector. Over the out‐of‐sample 1976–86 period, only the expert outperformed the conditional qualitative forecast. The accuracy of the conditional forecast improved as the bayesian parameters were updated.