Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting

  • Pierre Alquier
  • Xiaoyin Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7569)

Abstract

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.

Keywords

Statistical learning theory time series quantile regression GDP forecasting PAC-Bayesian bounds oracle inequalities weak dependence confidence intervals business surveys 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pierre Alquier
    • 1
    • 3
  • Xiaoyin Li
    • 2
  1. 1.LPMA (Université Paris 7)ParisFrance
  2. 2.Laboratoire de Mathématiques (Université de Cergy-Pontoise)UCP site Saint-MartinCergy-PontoiseFrance
  3. 3.CREST (ENSAE)France

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