Combined forecasts from linear and nonlinear time series models
Combined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)linear model. The methods are applied to data from two kinds of disciplines: the Canadian lynx and sunspot series from the natural sciences, and Nelson-Plosser's U.S. series from economics. It is shown that the combined forecasts perform well, especially with time varying coefficients. This result holds for out of sample performance for the sunspot and Canadian lynx number series, but it does not uniformly hold for economic time series.
(This abstract was borrowed from another version of this item.)
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
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.:
- 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.
- Clements, Michael P & Smith, Jeremy, 1996. "A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models," The Warwick Economics Research Paper Series (TWERPS) 464, University of Warwick, Department of Economics.
- Terui, Nobuhiko & van Dijk, Herman K., 2002.
"Combined forecasts from linear and nonlinear time series models,"
International Journal of Forecasting,
Elsevier, vol. 18(3), pages 421-438.
- N. Terui & Herman K. van Dijk, 2000. "Combined Forecasts from Linear and Nonlinear Time Series Models," Tinbergen Institute Discussion Papers 00-003/4, Tinbergen Institute.
- Terui, N. & van Dijk, H.K., 1999. "Combined forecasts from linear and nonlinear time series models," Econometric Institute Research Papers EI 9949-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- 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.
- 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.
- Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, vol. 44(1), pages 167-84, January.
- Dwight B. Crane & James R. Crotty, 1967. "A Two-Stage Forecasting Model: Exponential Smoothing and Multiple Regression," Management Science, INFORMS, vol. 13(8), pages B501-B507, April.
When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:18:y:2002:i:3:p:421-438. 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: (Zhang, Lei)
If references are entirely missing, you can add them using this form.