IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/6607.html
   My bibliography  Save this paper

A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series

Author

Listed:
  • James H. Stock
  • Mark W. Watson

Abstract

A forecasting comparison is undertaken in which 49 univariate forecasting methods, plus various forecast pooling procedures, are used to forecast 215 U.S. monthly macroeconomic time series at three forecasting horizons over the period 1959 - 1996. All forecasts simulate real time implementation, that is, they are fully recursive. The forecasting methods are based on four classes of models: autoregressions (with and without unit root pretests), exponential smoothing, artificial neural networks, and smooth transition autoregressions. The best overall performance of a single method is achieved by autoregressions with unit root pretests, but this performance can be improved when it is combined with the forecasts from other methods.

Suggested Citation

  • James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:6607
    Note: EFG ME
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w6607.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clive W. Granger & Timo Terasvirta & Heather M. Anderson, 1993. "Modeling Nonlinearity over the Business Cycle," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 311-326, National Bureau of Economic Research, Inc.
    2. Granger, Clive W J, 1993. "Strategies for Modelling Nonlinear Time-Series Relationships," The Economic Record, The Economic Society of Australia, vol. 69(206), pages 233-238, September.
    3. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
    4. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    5. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    6. McNees, Stephen K., 1990. "The role of judgment in macroeconomic forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 6(3), pages 287-299, October.
    7. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    8. Stock, James H, 1996. "VAR, Error Correction and Pretest Forecasts at Long Horizons," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 685-701, November.
    9. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    10. Terasvirta, Timo & Tjostheim, Dag & W.J. Granger, Clive, 1986. "Aspects of modelling nonlinear time series," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 48, pages 2917-2957, Elsevier.
    11. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    12. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, February.
    2. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    3. Massimiliano Marcellino, "undated". "Forecast pooling for short time series of macroeconomic variables," Working Papers 212, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    6. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    7. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    8. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    9. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    10. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    11. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    12. Yvon Fauvel & Alain Paquet & Christian Zimmermann, 1999. "A Survey on Interest Rate Forecasting," Cahiers de recherche CREFE / CREFE Working Papers 87, CREFE, Université du Québec à Montréal.
    13. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    14. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    15. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
    16. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    17. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    18. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2022. "Globalization, long memory, and real interest rate convergence: a historical perspective," Empirical Economics, Springer, vol. 63(5), pages 2331-2355, November.
    19. Rossi, Barbara, 2005. "Confidence Intervals for Half-Life Deviations From Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 432-442, October.
    20. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:6607. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.