Quantile regression models with factor-augmented predictors and information criterion

Tomohiro Ando, Ruey S. Tsay

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-24
Number of pages24
JournalEconometrics Journal
Volume14
Issue number1
DOIs
Publication statusPublished - 2011 Feb 1

Keywords

  • Approximate factor models
  • Generated regressors
  • Information-theoretic approach
  • Panel data
  • Quantiles

ASJC Scopus subject areas

  • Economics and Econometrics

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