Sökning: onr:"swepub:oai:hhs.se:1162052150006056" >
Linear models, smoo...
Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series : a re-examination
-
- Medeiros, Marcelo C. (författare)
- Pontifical Catholic University of Rio de Janeiro
-
- Teräsvirta, Timo (författare)
- Stockholm School of Economics,Handelshögskolan i Stockholm
-
- van Dijk, Dick (författare)
- Erasmus University Rotterdam (Netherlands, Rotterdam) - EUR
-
(creator_code:org_t)
- Elsevier, 2005
- 2005
- Engelska.
-
Ingår i: International Journal of Forecasting. - : Elsevier. - 1872-8200 .- 0169-2070. ; 21:4, s. 755-774
- Relaterad länk:
-
https://www.econstor...
-
visa fler...
-
https://research.hhs...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas