Online Forecast Combination for Dependent Heterogeneous Data
AbstractThis paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific conditions on the nature of time variation. Some experimental evidence to confirm the results is provided.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0718.
Date of creation: Apr 2007
Date of revision:
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Web page: http://www.econ.cam.ac.uk/index.htm
Forecast Combination; Model Selection; Multiplicative Update; Non-asymptotic Bound; On-line Learning.;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-04-28 (All new papers)
- NEP-ECM-2007-04-28 (Econometrics)
- NEP-ETS-2007-04-28 (Econometric Time Series)
- NEP-FOR-2007-04-28 (Forecasting)
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