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Maximum likelihood factor analysis with rank-deficient sample covariance matrices

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Author Info

  • Robertson, Donald
  • Symons, James

Abstract

This paper characterises completely the circumstances in which maximum likelihood estimation of the factor model is feasible when the sample covariance matrix is rank deficient. This situation will arise when the number of variables exceeds the number of observations.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 98 (2007)
Issue (Month): 4 (April)
Pages: 813-828

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Handle: RePEc:eee:jmvana:v:98:y:2007:i:4:p:813-828

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Related research

Keywords: Factor analysis Maximum likelihood;

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Cited by:
  1. Pesaran, M. Hashem & Tosetti, Elisa, 2007. "Large Panels with Common Factors and Spatial Correlations," IZA Discussion Papers 3032, Institute for the Study of Labor (IZA).
  2. Sarafidis, Vasilis & Wansbeek, Tom, 2010. "Cross-sectional Dependence in Panel Data Analysis," MPRA Paper 20367, University Library of Munich, Germany.
  3. Natalia Bailey & Sean Holly & N. Hashem Pesaran, 2013. "A Two Stage Approach to Spatiotemporal Analysis with Strong and weak cross Sectional Dependence," Cambridge Working Papers in Economics 1362, Faculty of Economics, University of Cambridge.

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