Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination
Timo Teräsvirta (),
Dick van Dijk () and
Marcelo Medeiros ()
No 485, Textos para discussão from Department of Economics PUC-Rio (Brazil)
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 JEL Codes: C22; C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ets and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Published in International Journal of Forecasting, v.21, 2005
Downloads: (external link)
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 re-examination (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rio:texdis:485
Access Statistics for this paper
More papers in Textos para discussão from Department of Economics PUC-Rio (Brazil) Contact information at EDIRC.
Bibliographic data for series maintained by ().