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Collinearity and the use of latent root regression for combining GNP forecasts
Author(s) -
Guerard John B.,
Clemen Robert T.
Publication year - 1989
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.3980080308
Subject(s) - collinearity , ordinary least squares , econometrics , statistics , regression , mathematics , regression analysis , latent variable , estimation , partial least squares regression , economics , management
In combining economic forecasts a problem often faced is that the individual forecasts display some degree of dependence. We discuss latent root regression for combining collinear GNP forecasts. Our results indicate that latent root regression produces more efficient combining weight estimates (regression parameter estimates) than ordinary least squares estimation (OLS), although out‐of‐sample forecasting performance is comparable to OLS.