lundi 19 mars 2012

Vendredi 13 avril : Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting,

Lieu ENGREF
Salle 7
12h30-13h30
Vendredi 13 avril 2012

Pierre Alquier (Paris 7) et X.Li (Université de Cergy)
Prediction of Quantiles by Statistical
Learning and Application to GDP Forecasting

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 (also known as Exponentially Weighted aggregate) 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 Koenker and Bassett (1978), 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.