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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting
verfasst von : Pierre Alquier, Xiaoyin Li
Erschienen in: Discovery Science
Verlag: Springer Berlin Heidelberg
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In this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of [1], this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results.