Model selection for generalized linear models with factor-augmented predictors

Tomohiro Ando, Ruey S. Tsay

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper considers generalized linear models in a data-rich environment in which a large number of potentially useful explanatory variables are available. In particular, it deals with the case that the sample size and the number of explanatory variables are of similar sizes. We adopt the idea that the relevant information of explanatory variables concerning the dependent variable can be represented by a small number of common factors and investigate the issue of selecting the number of common factors while taking into account the effect of estimated regressors. We develop an information criterion under model mis-specification for both the distributional and structural assumptions and show that the proposed criterion is a natural extension of the Akaike information criterion (AIC). Simulations and empirical data analysis demonstrate that the proposed new criterion outperforms the AIC and Bayesian information criterion.

Original languageEnglish
Pages (from-to)207-235
Number of pages29
JournalApplied Stochastic Models in Business and Industry
Volume25
Issue number3
DOIs
Publication statusPublished - 2009 Jan 1

Keywords

  • Approximate factor model
  • Common factor
  • Estimated regressor
  • Panel data
  • Predictive measure

ASJC Scopus subject areas

  • Modelling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Model selection for generalized linear models with factor-augmented predictors'. Together they form a unique fingerprint.

  • Cite this