Advanced Search
MyIDEAS: Login to save this paper or follow this series

Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows

Contents:

Author Info

  • Assenmacher-Wesche, Katrin

    ()
    (University of Bern)

  • Pesaran, M. Hashem

    ()
    (University of Cambridge)

Abstract

We investigate the effect of forecast uncertainty in a cointegrating vector error correction model for Switzerland. Forecast uncertainty is evaluated in three different dimensions. First, we investigate the effect on forecasting performance of averaging over forecasts from different models. Second, we look at different estimation windows. We find that averaging over estimation windows is at least as effective as averaging over different models and both complement each other. Third, we explore whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the effect of the weighting scheme on forecast accuracy is small in our application.

Download Info

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.
File URL: http://ftp.iza.org/dp3071.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3071.

as in new window
Length: 55 pages
Date of creation: Sep 2007
Date of revision:
Publication status: published in: National Institute Economic Review, 2008, 203 (1), 91–108
Handle: RePEc:iza:izadps:dp3071

Contact details of provider:
Postal: IZA, P.O. Box 7240, D-53072 Bonn, Germany
Phone: +49 228 3894 223
Fax: +49 228 3894 180
Web page: http://www.iza.org

Order Information:
Postal: IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany
Email:

Related research

Keywords: choice of observation window; long-run structural vector autoregression; Bayesian model averaging;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

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.:
as in new window
  1. Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2009. "A VECX* model of the Swiss economy," Economic Studies 2009-06, Swiss National Bank.
  2. Dées, Stéphane & di Mauro, Filippo & Pesaran, Hashem & Smith, Vanessa, 2005. "Exploring the international linkages of the euro area: a global VAR analysis," Working Paper Series, European Central Bank 0568, European Central Bank.
  3. Pesaran, M.H. & Schleicher, C. & Zaffaroni, P., 2008. "Model Averaging in Risk Management with an Application to Futures Markets," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 0808, Faculty of Economics, University of Cambridge.
  4. Pesaran, M. H. & Shin, Y. & Smith, R. J., 1997. "Structural Analysis of Vector Error Correction Models with Exogenous I(1) Variables," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 9706, Faculty of Economics, University of Cambridge.
  5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 134-44, January.
  6. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CESifo Working Paper Series 1237, CESifo Group Munich.
  7. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521634809.
  8. A Garratt & K Lee & M Pesaran & Yongcheol Shin, 2004. "A long run structural macroeconometric model of the UK," ESE Discussion Papers, Edinburgh School of Economics, University of Edinburgh 35, Edinburgh School of Economics, University of Edinburgh.
  9. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, Elsevier, vol. 137(1), pages 134-161, March.
  10. Graham Elliott & Thomas J. Rothenberg & James H. Stock, 1992. "Efficient Tests for an Autoregressive Unit Root," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0130, National Bureau of Economic Research, Inc.
  11. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier, Elsevier.
  12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
  13. Garratt A. & Lee K. & Pesaran M.H. & Shin Y., 2003. "Forecast Uncertainties in Macroeconomic Modeling: An Application to the U.K. Economy," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 98, pages 829-838, January.
  14. Yang, Yuhong, 2004. "Combining Forecasting Procedures: Some Theoretical Results," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 20(01), pages 176-222, February.
  15. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 20(3), pages 161-79, April.
  16. Garratt, Anthony & Lee, Kevin & Pesaran, M. Hashem & Shin, Yongcheol, 2012. "Global and National Macroeconometric Modelling: A Long-Run Structural Approach," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780199650460, October.
  17. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier, Elsevier.
  18. Philippe J. Deschamps, 2008. "Comparing smooth transition and Markov switching autoregressive models of US unemployment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 23(4), pages 435-462.
  19. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, The MIT Press, edition 1, volume 1, number 0262531895, December.
  20. Sancetta, A., 2007. "Online Forecast Combination for Dependent Heterogeneous Data," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 0718, Faculty of Economics, University of Cambridge.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp3071. 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: (Mark Fallak).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.