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Reducing uncertainty in short‐term projections: Linkage of monthly and quarterly models
Author(s) -
Corrado Carol,
Greene Mark
Publication year - 1988
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980070202
Subject(s) - pooling , covariance , econometrics , kalman filter , term (time) , baseline (sea) , computer science , predictability , forecast error , variance (accounting) , linkage (software) , statistics , mathematics , economics , artificial intelligence , biochemistry , physics , oceanography , accounting , chemistry , quantum mechanics , gene , geology
This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non‐recursive Kalman filtering) where a baseline quarterly model forecast is modified through ‘add‐factors’ or ‘constant adjustments’. The procedure ‘automatically’ constructs these adjustments in a covariance‐minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures.