IDEAS home Printed from https://ideas.repec.org/p/ihs/ihsesp/79.html
   My bibliography  Save this paper

Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning

Author

Listed:
  • Crespo-Cuaresma, Jesus

    (Institute for Advanced Studies, Vienna)

Abstract

A two-regime self-exciting threshold autoregressive process is estimated for quarterly aggregate GDP of the fifteen countries that compose the European Union, and the forecasts from this nonlinear model are compared, by means of a Monte Carlo simulation, with those from a simple autoregressive model, whose lag length is chosen to minimize Akaike's AIC criterion. The results are very negative for the SETAR model when the Monte Carlo procedure is used to generate multi-step forecasts. When the "naive" procedure of generating forecasts is used, the results are surprisingly better for the SETAR model in long-term predictions. Due to the characteristics of the residuals, a bootstrapping method of forecasting was also used, yielding even poorer results for the nonlinear model.

Suggested Citation

  • Crespo-Cuaresma, Jesus, 2000. "Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning," Economics Series 79, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:79
    as

    Download full text from publisher

    File URL: http://www.ihs.ac.at/publications/eco/es-79.pdf
    File Function: First version, 2000
    Download Restriction: no

    References listed on IDEAS

    as
    1. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
    2. De Gooijer, Jan G. & Kumar, Kuldeep, 1992. "Some recent developments in non-linear time series modelling, testing, and forecasting," International Journal of Forecasting, Elsevier, vol. 8(2), pages 135-156, October.
    3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, December.
    4. Michael Pippenger & Gregory Goering, 1998. "Exchange Rate Forecasting: Results from a Threshold Autoregressive Model," Open Economies Review, Springer, vol. 9(2), pages 157-170, April.
    5. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
    6. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    7. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, pages 1383-1414.
    8. Philip Rothman, "undated". "Forecasting Asymmetric Unemployment Rates," Working Papers 9618, East Carolina University, Department of Economics.
    9. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
    10. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Nonlinear Time Series Models; SETAR Models; Forecasting;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:ihs:ihsesp:79. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Doris Szoncsitz). General contact details of provider: http://edirc.repec.org/data/deihsat.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.