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THE FORECASTING PERFORMANCE OF SETAR MODELS: AN EMPIRICAL APPLICATION
Author(s) -
Boero Gianna,
Lampis Federico
Publication year - 2017
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/boer.12068
Subject(s) - setar , econometrics , economics , computer science , index (typography) , point (geometry) , interval (graph theory) , nonlinear system , linear model , industrial production , recession , mathematics , machine learning , time series , macroeconomics , autoregressive integrated moving average , star model , combinatorics , world wide web , physics , quantum mechanics , geometry
The aim of this paper is to evaluate the forecasting performance of SETAR models with an application to the Industrial Production Index (IPI) of four major European countries over a period which includes the last Great Recession. Both point and interval forecasts are considered at different horizons against those obtained from two linear models. We follow the approach suggested by Teräsvirta et al. (2005) according to which a dynamic specification may improve the forecast performance of the nonlinear models with respect to the linear models. We re‐specify the models every twelve months and we find that the advantages of this procedure are particularly evident in the forecast rounds immediately following the re‐specification.