Estimation of approximate factor models: is it important to have a large number of variables?

Chris Heaton, Victor Solo

Research output: Contribution to journalArticle


The use of principal component techniques to estimate approximate factor models with large cross-sectional dimension is now well established. However, recent work by Inklaar, R. J., J. Jacobs and W. Romp (2003) and Boivin, J. and S. Ng (2005) has cast some doubt on the importance of a large cross-sectional dimension for the precision of the estimates. This paper presents some new theory for approximate factor model estimation. Consistency is proved and rates of convergence are derived under conditions that allow for a greater degree of cross-correlation in the model disturbances than previously published results. The rates of convergence depend on the rate at which the cross-sectional correlation of the model disturbances grows as the cross-sectional dimension grows. The consequences for applied economic analysis are discussed.
Original languageEnglish
Pages (from-to)1-31
Number of pages31
JournalMacquarie economics research papers
Issue number5
Publication statusPublished - 2006


  • factor analysis
  • time series models
  • principal components


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