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A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets

  • GUO-FITOUSSI, Liang
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    Abstract In this paper, we compare the properties of the main criteria proposed for selecting the number of factors in dynamic factor model in a small sample. Both static and dynamic factor numbers' selection rules are studied. Simulations show that the GR ratio proposed by Ahn and Horenstein (2013) and the criterion proposed by Onatski (2010) outperform the others. Furthermore, the two criteria can select accurately the number of static factors in a dynamic factors design. Also, the criteria proposed by Hallin and Liska (2007) and Breitung and Pigorsch (2009) correctly select the number of dynamic factors in most cases. However, empirical application show most criteria select only one factor in presence of one strong factor.

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    File URL: https://mpra.ub.uni-muenchen.de/50005/1/MPRA_paper_50005.pdf
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 50005.

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    Date of creation: Sep 2013
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    Handle: RePEc:pra:mprapa:50005
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    1. 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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    3. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
    4. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    5. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models," LEM Papers Series 2007/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2013. "Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 64-79, 02.
    7. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
    8. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
    9. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    10. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
    11. D’Agostino, Antonello & Giannone, Domenico, 2006. "Comparing alternative predictors based on large-panel factor models," Working Paper Series 0680, European Central Bank.
    12. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1319-1347, October.
    13. Koopman, Siem Jan & van der Wel, Michel, 2013. "Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model," International Journal of Forecasting, Elsevier, vol. 29(4), pages 676-694.
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    16. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
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    19. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, 09.
    20. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, 05.
    21. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    22. Stephen G. Donald, 1997. "Inference Concerning the Number of Factors in a Multivariate Nonparametric Relationship," Econometrica, Econometric Society, vol. 65(1), pages 103-132, January.
    23. Andrea Cipollini & Giuseppe Missaglia, 2007. "Dynamic Factor analysis of industry sector default rates and implication for Portfolio Credit Risk Modelling," Center for Economic Research (RECent) 007, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    24. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
    25. Cragg, John G. & Donald, Stephen G., 1997. "Inferring the rank of a matrix," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 223-250.
    26. George Kapetanios, 2004. "A New Method for Determining the Number of Factors in Factor Models with Large Datasets," Working Papers 525, Queen Mary University of London, School of Economics and Finance.
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