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Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

Author

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  • Teräsvirta, Timo

    (Dept. of Economic Statistics, Stockholm School of Economics)

  • van Dijk, Dick

    (Econometric Institute, Erasmus University Rotterdam)

  • Medeiros, Marcelo

    (Department of Economics, Pontifical Catholic University of Rio de Janeiro)

Abstract

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.

Suggested Citation

  • Teräsvirta, Timo & van Dijk, Dick & Medeiros, Marcelo, 2004. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," SSE/EFI Working Paper Series in Economics and Finance 561, Stockholm School of Economics, revised 09 Nov 2004.
  • Handle: RePEc:hhs:hastef:0561
    Note: The paper will appear with Discussion by Professor Alfonso Novales and a reply by the authors.
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    More about this item

    Keywords

    forecast combination; forecast evaluation; neural network model; nonlinear modelling; nonlinear forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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