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On SETAR non-linearity and forecasting

Author

Listed:
  • Dick van Dijk

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Philip Hans Franses

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Michael P. Clements

    (Department of Economics, University of Warwick, UK)

  • Jeremy Smith

    (Department of Economics, University of Warwick, UK)

Abstract

We compare linear autoregressive (AR) models and self-exciting threshold autoregressive (SETAR) models in terms of their point forecast performance, and their ability to characterize the uncertainty surrounding those forecasts, i.e. interval or density forecasts. A two-regime SETAR process is used as the data-generating process in an extensive set of Monte Carlo simulations, and we consider the discriminatory power of recently developed methods of forecast evaluation for different degrees of non-linearity. We find that the interval and density evaluation methods are unlikely to show the linear model to be deficient on samples of the size typical for macroeconomic data. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
  • Handle: RePEc:jof:jforec:v:22:y:2003:i:5:p:359-375
    DOI: 10.1002/for.863
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    References listed on IDEAS

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    Cited by:

    1. GIOT, Pierre & PETITJEAN, Mikael, 2005. "Dynamic asset allocation between stocks and bonds using the Bond-Equity Yield Ratio," CORE Discussion Papers 2005010, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Duarte, A. & Venetis, I. & Payá, I., 2004. "Curva de rendimientos y crecimiento de la producción real en la UEM: eficiencia y estabilidad predictiva./Yield Curve and Real Output Growth in the EMU: Efficiency and Predictive Stability," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 22, pages 1-21, Abril.
    3. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Papers 200923, University of Pretoria, Department of Economics.
    4. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    5. Hui Feng & Jia Liu, 2003. "A SETAR model for Canadian GDP: non-linearities and forecast comparisons," Applied Economics, Taylor & Francis Journals, vol. 35(18), pages 1957-1964.
    6. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    7. Ramazan Gencay & Ege Yazgan, 2017. "When Are Wavelets Useful Forecasters?," Working Papers 1704, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    8. Giot, Pierre & Petitjean, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 289-305.
    9. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle," Working Papers 1206, University of Guelph, Department of Economics and Finance.
    10. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Bermejo, Miguel Ángel & Peña, Daniel & Sánchez, Ismael, 2009. "Graphical identification of TAR models," DES - Working Papers. Statistics and Econometrics. WS ws097723, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    13. repec:sbe:breart:v:25:y:2005:i:2:a:2504 is not listed on IDEAS
    14. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    15. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    16. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    17. Bec, Frédérique & Bouabdallah, Othman & Ferrara, Laurent, 2014. "The way out of recessions: A forecasting analysis for some Euro area countries," International Journal of Forecasting, Elsevier, vol. 30(3), pages 539-549.
    18. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2011. "The European Way Out of Recessions," THEMA Working Papers 2011-23, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    19. Rodriguez, Nestor & Eales, James S., 2015. "Structural Change via Threshold Effects: Estimating U.S. Meat Demand Using Smooth Transition Functions and the Effects of More Women in the Labor Force," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206522, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    20. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, EconWPA.
    21. Pedro M.D.C.B. Gouveia & Denise R. Osborn & Paulo M.M. Rodrigues, 2008. "Comparing Seasonal Forecasts of Industrial Production," Centre for Growth and Business Cycle Research Discussion Paper Series 102, Economics, The Univeristy of Manchester.
    22. Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.
    23. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.

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