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Predicting Turning Points through the Integration of Multiple Models

Author

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  • Li, David T
  • Dorfman, Jeffrey H

Abstract

A new method for forming composite turning point (or other qualitative) forecasts is proposed. Rather than forming composite forecasts by the standard Bayesian approach with weights proportional to each model's posterior odds, weights are assigned to the individual models in proportion to the probability of each model's having the correct turning point prediction. These probabilities are generated by logit models estimated with data on the models' past turning point forecasts. An empirical application to GNP/GDP forecasting of eighteen OECD countries demonstrates the potential benefits of the procedure.

Suggested Citation

  • Li, David T & Dorfman, Jeffrey H, 1996. "Predicting Turning Points through the Integration of Multiple Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 421-428, October.
    • Handle: RePEc:bes:jnlbes:v:14:y:1996:i:4:p:421-28
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    Cited by:

    1. Chua, Chew Lian & Tsiaplias, Sarantis, 2011. "Predicting economic contractions and expansions with the aid of professional forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 438-451.
    2. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    3. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    4. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    5. Pena, Daniel & Poncela, Pilar, 2004. "Forecasting with nonstationary dynamic factor models," Journal of Econometrics, Elsevier, vol. 119(2), pages 291-321, April.
    6. Camacho, Maximo & Pérez Quirós, Gabriel, 2000. "This is what the US leading indicators lead," Working Paper Series 0027, European Central Bank.
    7. David Bock & Eva Andersson & Marianne Frisén, 2005. "Statistical surveillance of cyclical processes with application to turns in business cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 465-490.
    8. E. Andersson & D. Bock & M. Frisen, 2006. "Some statistical aspects of methods for detection of turning points in business cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(3), pages 257-278.
    9. E. Andersson, 2002. "Monitoring cyclical processes. A non-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 973-990.

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