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Forecasting industrial production using non‐linear methods
Author(s) -
Byers J. D.,
Peel D. A.
Publication year - 1995
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.3980140402
Subject(s) - autoregressive model , bilinear interpolation , exploit , econometrics , industrial production , production (economics) , nonlinear system , parametric statistics , linearity , series (stratigraphy) , computer science , parametric model , linear model , economics , mathematics , statistics , machine learning , engineering , macroeconomics , paleontology , physics , computer security , quantum mechanics , biology , electrical engineering , computer vision
Numerous theoretical models suggests that business cycles involve nonlinear processes. In this paper we examine whether two parametric, nonlinear time‐series models—the bilinear and threshold models—can exploit apparent non‐linearity in industrial production to provide forecasts superior to those derived from the standard autoregressive models.