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

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Author Info
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)

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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.

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Publisher Info
Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 561.

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Length: 36 pages
Date of creation: 14 Jul 2004
Date of revision: 04 Nov 2004
Publication status: Published in International Journal of Forecasting, 2005, pages 755-774.
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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Related research
Keywords: forecast combination; forecast evaluation; neural network model; nonlinear modelling; nonlinear forecasting;

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November. [Downloadable!] (restricted)
    Other versions:
  2. Clements, Michael P & Smith, Jeremy, 1999. "A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 123-41, March-Apr. [Downloadable!]
    Other versions:
  3. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May. [Downloadable!] (restricted)
    Other versions:
  4. B. Siliverstovs & D.J. Van Dijk, 2003. "Forecasting industrial production with linear, nonlinear and structural change models," Econometric Institute Report 321, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
    Other versions:
  5. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June. [Downloadable!] (restricted)
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  6. Boero, Gianna & Marrocu, Emanuela, 2002. "The Performance of Non-linear Exchange Rate Models: A Forecasting Comparison," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 513-42, November.
  7. Michael P. Clements & Hans-Martin Krolzig, 1998. "A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages C47-C75.
  8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June. [Downloadable!] (restricted)
  9. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March. [Downloadable!] (restricted)
  10. Norman R. Swanson & Halbert White, 1992. "A Model Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," University of California at San Diego, Economics Working Paper Series 92-39, Department of Economics, UC San Diego.
    Other versions:
  11. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446. [Downloadable!] (restricted)
  12. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De. [Downloadable!] (restricted)
  13. G. Rech & T. Teräsvirta & R. Tschernig, . "A Simple variable selection technique for nonlinear models," Sonderforschungsbereich 373 1999-26, Humboldt Universitaet Berlin.
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  14. Marcellino, Massimiliano, 2002. "Instability and Non-Linearity in the EMU," CEPR Discussion Papers 3312, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  15. Skalin, Joakim & Ter svirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(02), pages 202-241, April. [Downloadable!]
  16. Timo TerŠsvirta & Chien-Fu Lin & Clive W.J. Granger, 1991. "Power of the Neural Network Linearity Test," University of California at San Diego, Economics Working Paper Series 91-01, Department of Economics, UC San Diego.
  17. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69. [Downloadable!] (restricted)
  18. Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," Working Paper Series in Economics and Finance 508, Stockholm School of Economics. [Downloadable!]
    Other versions:
  19. Rech, Gianluigi, 2002. "Forecasting with artificial neural network models," Working Paper Series in Economics and Finance 491, Stockholm School of Economics. [Downloadable!]
  20. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001. [Downloadable!]
    Other versions:
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Balagtas, Joseph V. & Holt, Matthew T., 2006. "Unit Roots, TV-STARs, and the Commodity Terms of Trade: A Further Assessment of the Prebisch-Singer Hypothesis," 2006 Annual meeting, July 23-26, Long Beach, CA 21405, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association). [Downloadable!]
  2. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2005. "On the predictability of common risk factors in the US and UK interest rate swap markets:Evidence from non-linear and linear models," Keele Economics Research Papers KERP 2005/13, Centre for Economic Research, Keele University. [Downloadable!]
    Other versions:
  3. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2009. "Non-linear predictability in stock and bond returns: when and where is it exploitable?," Working Papers 2008-010, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  4. Costas Milas & Ilias Lekkos & Theodore Panagiotidis, 2006. "Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models," Keele Economics Research Papers KERP 2006/05, Centre for Economic Research, Keele University. [Downloadable!]
    Other versions:
  5. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," Ifo Working Paper Series Ifo Working Paper No. 57, Ifo Institute for Economic Research at the University of Munich. [Downloadable!]
  6. Ralf Becker & Denise Osborn, 2007. "Weighted smooth transition regressions," The School of Economics Discussion Paper Series 0724, Economics, The University of Manchester. [Downloadable!]
  7. Jonathan B. Hill, 2004. "Consistent and Non-Degenerate Model Specification Tests Against Smooth Transition Alternatives," Working Papers 0406, Florida International University, Department of Economics. [Downloadable!]
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