This paper considers forecasts of the distribution of data whose distribution function is possibly time varying. The forecast is achieved via time varying combinations of experts’ forecasts. We derive theoretical worse case bounds for general algorithms based on multiplicative updates of the combination weights. The bounds are useful to study the properties of forecast combinations when data are nonstationary and there is no unique best model. An application with an empirical study is used to highlight the results in practice.
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Find related papers by JEL classification: C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
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Hansen, Bruce E, 1994.
"Autoregressive Conditional Density Estimation,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-30, August.
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David F. Hendry & Michael P. Clements, 2004.
"Pooling of forecasts,"
Econometrics Journal,
Royal Economic Society, vol. 7(1), pages 1-31, 06.
[Downloadable!] (restricted)
Other versions:
David Hendry & Michael P. Clements, 2001.
"Pooling of Forecasts,"
Economics Papers
2002-W9, Economics Group, Nuffield College, University of Oxford.
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