@article {IOPORT.05932395,
    author       = {Ando, Tomohiro and Tsay, Ruey S.},
    title        = {Quantile regression models with factor-augmented predictors and information criterion.},
    year         = {2011},
    journal      = {The Econometrics Journal},
    volume       = {14},
    number       = {1},
    issn         = {1368-4221},
    pages        = {1-24},
    publisher    = {The Royal Economic Society, London; Blackwell Publishers Ltd., Oxford},
    doi          = {10.1111/j.1368-423X.2010.00320.x},
    abstract     = {Summary: For situations with a large number of series, $N$, each with $T$ observations and each containing a certain amount of information for prediction of the variable of interest, we propose a new statistical modelling methodology that first estimates the common factors from a panel of data using principal component analysis and then employs the estimated factors in a standard quantile regression. A crucial step in the model-building process is the selection of a good model among many possible candidates. Taking into account the effect of estimated regressors, we develop an information-theoretic criterion. We also investigate the criterion when there is no estimated regressors. Results of Monte Carlo simulations demonstrate that the proposed criterion performs well in a wide range of situations.},
    identifier   = {05932395},
}