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Forecasting with difference-stationary and trend-stationary models

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Author Info

  • MICHAEL P. CLEMENTS
  • DAVID F.HENDRY

Abstract

While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two methods of analysis are employed: an empirical forecast accuracy comparison of the two models, and a Monte Carlo study. The latter allows us to control for factors that may otherwise undermine the performance of the non-linear models.

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 4 (2001)
Issue (Month): 1 ()
Pages: S1-S19

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Handle: RePEc:ect:emjrnl:v:4:y:2001:i:1:p:s1-s19

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Related research

Keywords: Business cycles; Monte Carlo simulation; Nonlinear time series; Prediction; Regime shifts.;

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Cited by:
  1. Hamid Baghestani, 2009. "Evaluating random walk forecasts of exchange rates," Studies in Economics and Finance, Emerald Group Publishing, vol. 26(3), pages 171-181, August.
  2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
  3. Peter C.B. Phillips, 2003. "Laws and Limits of Econometrics," Cowles Foundation Discussion Papers 1397, Cowles Foundation for Research in Economics, Yale University.
  4. repec:oxf:wpaper:078 is not listed on IDEAS
  5. David Griffiths, 2004. "The big problem of forecasting small change," Applied Economics, Taylor and Francis Journals, vol. 36(19), pages 2195-2207.
  6. Neil R. Ericsson, 2000. "Predictable uncertainty in economic forecasting," International Finance Discussion Papers 695, Board of Governors of the Federal Reserve System (U.S.).
  7. Guillaume Chevillon, 2004. ""Weak" trends for inference and forecasting in finite samples," Documents de Travail de l'OFCE 2004-12, Observatoire Francais des Conjonctures Economiques (OFCE).
  8. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
  9. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
  10. Yan-Leung Cheung & Yin-Wong Cheung & Alan T. K. Wan, 2009. "A High-Low Model of Daily Stock Price Ranges," Working Papers 032009, Hong Kong Institute for Monetary Research.
  11. David Harvey & Terence Mills, 2002. "Unit roots and double smooth transitions," Journal of Applied Statistics, Taylor and Francis Journals, vol. 29(5), pages 675-683.
  12. Guillaume Chevillon, 2004. "`Weak` trends for inference and forecasting in finite samples," Economics Series Working Papers 210, University of Oxford, Department of Economics.

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