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Combination forecasts of output growth in a seven-country data set

  • Mark W. Watson

    (Woodrow Wilson School and Department of Economics, Princeton University and the National Bureau of Economic Research, USA)

  • James H. Stock

    (Department of Economics, Harvard University and the National Bureau of Economic Research, USA)

This paper uses forecast combination methods to forecast output growth in a seven-country quarterly economic data set covering 1959-1999, with up to 73 predictors per country. Although the forecasts based on individual predictors are unstable over time and across countries, and on average perform worse than an autoregressive benchmark, the combination forecasts often improve upon autoregressive forecasts. Despite the unstable performance of the constituent forecasts, the most successful combination forecasts, like the mean, are the least sensitive to the recent performance of the individual forecasts. While consistent with other evidence on the success of simple combination forecasts, this finding is difficult to explain using the theory of combination forecasting in a stationary environment. Copyright © 2004 John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 23 (2004)
Issue (Month): 6 ()
Pages: 405-430

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Handle: RePEc:jof:jforec:v:23:y:2004:i:6:p:405-430
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