Quantile regression models with factor-augmented predictors and information criterion

Tomohiro Ando, Ruey S. Tsay

研究成果: Article査読

18 被引用数 (Scopus)

抄録

For situations with a large number of series,N, each withTobservations 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.

本文言語English
ページ(範囲)1-24
ページ数24
ジャーナルEconometrics Journal
14
1
DOI
出版ステータスPublished - 2011 2

ASJC Scopus subject areas

  • Economics and Econometrics

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