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GMM Estimation of the Number of Latent Factors

  • Perez, Marcos
  • Ahn, Seung Chan

We propose a generalized method of moment (GMM) estimator of the number of latent factors in linear factor models. The method is appropriate for panels a large (small) number of cross-section observations and a small (large) number of time-series observations. It is robust to heteroskedasticity and time series autocorrelation of the idiosyncratic components. All necessary procedures are similar to three stage least squares, so they are computationally easy to use. In addition, the method can be used to determine what observable variables are correlated with the latent factors without estimating them. Our Monte Carlo experiments show that the proposed estimator has good finite-sample properties. As an application of the method, we estimate the number of factors in the US stock market. Our results indicate that the US stock returns are explained by three factors. One of the three latent factors is not captured by the factors proposed by Chen Roll and Ross 1986 and Fama and French 1996.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 4862.

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Date of creation: 09 Sep 2007
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Handle: RePEc:pra:mprapa:4862
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  1. Lewbel, Arthur, 1991. "The Rank of Demand Systems: Theory and Nonparametric Estimation," Econometrica, Econometric Society, vol. 59(3), pages 711-30, May.
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  5. Brown, Stephen J & Weinstein, Mark I, 1983. " A New Approach to Testing Asset Pricing Models: The Bilinear Paradigm," Journal of Finance, American Finance Association, vol. 38(3), pages 711-43, June.
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  12. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
  13. K. J├Âreskog, 1967. "Some contributions to maximum likelihood factor analysis," Psychometrika, Springer, vol. 32(4), pages 443-482, December.
  14. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
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  16. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  17. Lehmann, Bruce N. & Modest, David M., 1988. "The empirical foundations of the arbitrage pricing theory," Journal of Financial Economics, Elsevier, vol. 21(2), pages 213-254, September.
  18. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-91, September.
  19. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  20. Ferson, Wayne E. & Foerster, Stephen R., 1994. "Finite sample properties of the generalized method of moments in tests of conditional asset pricing models," Journal of Financial Economics, Elsevier, vol. 36(1), pages 29-55, August.
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  22. Seung Ahn & Young Lee & Peter Schmidt, 2007. "Stochastic frontier models with multiple time-varying individual effects," Journal of Productivity Analysis, Springer, vol. 27(1), pages 1-12, February.
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