Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting

Actes d’une conférence

Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting

Année

2012

Auteurs

Abstract

n 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.

ALQUIER, P. et LI, X. (2012). Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting. Dans: 15th International Conference on Discovery Science (DS’12). Lyon: Springer Berlin Heidelberg, pp. 22-36.