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Efficient Estimation of Factor Models

  • In Choi


    (Department of Economics, Sogang University, Seoul)

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This paper considers the factor model Xt = Ft + et. Assuming a nor- mal distribution for the idiosyncratic error et conditional on the factors fFtg, conditional maximum likelihood estimators of the factor and factor- loading spaces are derived. These estimators are called generalized prin- cipal component estimators (GPCEs) without the normality assumption. This paper derives the asymptotic distributions of the GPCEs of the fac- tor and factor-loading space. It is shown that variances of the GPCEs of the common components are smaller than those of the principal com- ponent estimators studied in Bai (2003). The approximate variance of the forecasting error using the GPCE-based factor estimates is derived and shown to be smaller than that based on the principal component es- timators. The feasible GPCE (FGPCE) of factor space is shown to be asymptotically equivalent to the GPCE. The GPCEs and FGPCEs are shown to be more efficient than the principal component estimators in finite samples.

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Paper provided by Research Institute for Market Economy, Sogang University in its series Working Papers with number 0701.

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Length: 43 pages
Date of creation: Mar 2007
Date of revision: Dec 2010
Handle: RePEc:sgo:wpaper:0701
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  1. 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.
  2. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
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  4. MOON, Hyungsik Roger & PERRON, Benoit., 2002. "Testing for a Unit Root in Panels with Dynamic Factors," Cahiers de recherche 2002-18, Universite de Montreal, Departement de sciences economiques.
  5. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
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  8. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  9. Jushan Bai & Serena Ng, 2001. "A Panic Attack on Unit Roots and Cointegration," Economics Working Paper Archive 469, The Johns Hopkins University,Department of Economics.
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  11. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, . "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  12. Kapetanios, George & Marcellino, Massimiliano, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.
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  14. Jones, Christopher S., 2001. "Extracting factors from heteroskedastic asset returns," Journal of Financial Economics, Elsevier, vol. 62(2), pages 293-325, November.
  15. Marc Hallin & Mario Forni & Marco Lippi & Lucrezia Reichlin, 2003. "Do financial variables help forecasting inflation and real activity in the Euro area ?," ULB Institutional Repository 2013/2123, ULB -- Universite Libre de Bruxelles.
  16. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  17. Peter C. B. Phillips & Donggyu Sul, 2003. "Dynamic panel estimation and homogeneity testing under cross section dependence *," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 217-259, 06.
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