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Combining forecasts using optimal combination weight and generalized autoregression

  • Jeong-Ryeol Kurz-Kim

    (Deutsche Bundesbank, Frankfurt am Main, Germany)

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    In this paper, we consider a combined forecast using an optimal combination weight in a generalized autoregression framework. The generalized autoregression provides not only a combined forecast but also an optimal combination weight for combining forecasts. By simulation, we find that short- and medium-horizon (as well as partly long-horizon) forecasts from the generalized autoregression using the optimal combination weight are more efficient than those from the usual autoregression in terms of the mean-squared forecast error. An empirical application with US gross domestic product confirms the simulation result. Copyright © 2008 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.1069
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 27 (2008)
    Issue (Month): 5 ()
    Pages: 419-432

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    Handle: RePEc:jof:jforec:v:27:y:2008:i:5:p:419-432
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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