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Nonlinear Forecasting Using Factor‐Augmented Models
Author(s) -
Giovannetti Bruno Cara
Publication year - 2013
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.1248
Subject(s) - econometrics , curse of dimensionality , computer science , covariate , nonlinear system , industrial production , factor analysis , set (abstract data type) , sample (material) , estimation , economics , artificial intelligence , macroeconomics , chemistry , physics , management , chromatography , quantum mechanics , programming language
Using factors in forecasting exercises reduces the dimensionality of the covariates set and, therefore, allows the forecaster to explore possible nonlinearities in the model. For an American macroeconomic dataset, I present evidence that the employment of nonlinear estimation methods can improve the out‐of‐sample forecasting accuracy for some macroeconomic variables, such as industrial production, employment, and Fed fund rate. Copyright © 2011 John Wiley & Sons, Ltd.