Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination
Timo Teräsvirta (),
Dick van Dijk () and
Marcelo Medeiros ()
No 561, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics
In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.
Keywords: forecast combination; forecast evaluation; neural network model; nonlinear modelling; nonlinear forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
Date: 2004-07-14, Revised 2004-11-09
Note: The paper will appear with Discussion by Professor Alfonso Novales and a reply by the authors.
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Published in International Journal of Forecasting, 2005, pages 755-774.
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Journal Article: Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination (2005)
Working Paper: Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0561
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