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A Robust Criterion for Determining the Number of Factors in Approximate Factor Models

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  • Lucia Alessi
  • Matteo Barigozzi
  • Marco Capasso

Abstract

We modify the criterion by Bai and Ng (2002) for determining the number of factors in approximate factor models. As in the original criterion, for any given number of factors we estimate the common and idiosyncratic components of the model by applying principal component analysis. We select the true number of factors as the number that minimizes the variance explained by the idiosyncratic component. In order to avoid overparametrization, minimization is subject to penalization. At this step, we modify the original procedure by multiplying the penalty function by a positive real number, which allows us to tune its penalizing power, by analogy with the method used by Hallin and Liška (2007) in the frequency domain. The contribution of this paper is twofold. First, our criterion retains the asymptotic properties of the original criterion, but corrects its tendency to overestimate the true number of factors. Second, we provide a computationally easy way to implement the new method by iteratively applying the original criterion. Monte Carlo simulations show that our criterion is in general more robust than the original one. A better performance is achieved in particular in the case of large idiosyncratic disturbances. These conditions are the most difficult for detecting a factor structure but are not unusual in the empirical context. Two applications on a macroeconomic and a financial dataset are also presented.

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Bibliographic Info

Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number 2009_023.

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Length: 21 p.
Date of creation: 2009
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Publication status: Published by:
Handle: RePEc:eca:wpaper:2009_023

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Keywords: Approximate factor models; information criterion; model selection.;

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  1. D'Agostino, Antonello & Giannone, Domenico, 2006. "Comparing Alternative Predictors Based on Large-Panel Factor Models," Research Technical Papers 14/RT/06, Central Bank of Ireland.
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Citations

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Cited by:
  1. Travaglini, Guido, 2011. "Principal Components and Factor Analysis. A Comparative Study," MPRA Paper 35486, University Library of Munich, Germany.
  2. Juan José Echavarría & Andrés gonzález & Enrique López & Norberto Rodríguez, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," BORRADORES DE ECONOMIA 009884, BANCO DE LA REPÚBLICA.
  3. Juan José Echavarría & Andrés González & Enrique López & Norberto Rodríguez, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.
  4. Xisong Jin & Francisco Nadal De Simone, 2013. "Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach," BCL working papers 82, Central Bank of Luxembourg.
  5. Enrique López & Fernando Tenjo Galarza & Diego H. Rodríguez H., 2012. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.

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