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Testing bias in professional forecasts
Author(s) -
Franses Philip Hans
Publication year - 2021
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.2765
Subject(s) - ordinary least squares , econometrics , instrumental variable , inflation (cosmology) , unemployment , survey of professional forecasters , economics , gross domestic product , estimation , least squares function approximation , regression , econometric model , statistics , mathematics , macroeconomics , monetary policy , physics , theoretical physics , management , estimator
Professional forecasters can rely on econometric models, on their personal expertise or on both. To accommodate for adjustments to model forecasts, this paper proposes to use two stage least squares (TSLS) (and not ordinary least squares [OLS]) for the familiar Mincer–Zarnowitz regression when examining bias in professional forecasts, where the instrumental variable is the consensus forecast. An illustration for 15 professional forecasters with their quotes for real gross domestic product (GDP) growth, inflation and unemployment for the United States documents the usefulness of this new estimation method. It also shows that TSLS suggests less bias than OLS does.