GUOFITOUSSI, Liang (2013): A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets.

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Abstract
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.
Item Type:  MPRA Paper 

Original Title:  A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets 
Language:  English 
Keywords:  dynamic factor model, factor numbers, small sample 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection 
Item ID:  50005 
Depositing User:  Mrs Liang GUOFITOUSSI 
Date Deposited:  20. Sep 2013 06:15 
Last Modified:  20. Sep 2013 06:35 
References:  Ahn, S.C. and A.R. Horenstein (2013), Eigenvalue Ratio Test for the Number of Factors. Econometrica, 81(3), 120327 Alessi L., Barigozzi M. and Capasso M. (2008), A robust criterion for determining the number of static factors in approximate factor models. Working paper No 903, European Central Bank. Amengual D. and Watson M.W. (2007), Consistent estimation of the number of dynamic factors in a large N and T panel. Journal of Business and Economic Statistics, 25, 9196. Bai J. and Ng S. (2002), Determining the number of factors in approximate factor models. Econometrica, 70, 191221. Bai, J., and S. Ng (2006), Determining the Number of Factors in Approximate Factor Models, errata. manuscript, Columbia University. Bai J. and Ng S. (2007), Determining the number of primitive shocks in factor models. Journal of Business and Economic Statistics, 25, 5260. Barhoumi K., Darné O. and Ferrara L. (2013), Testing the number of factors: An empirical assessment for forecasting purposes, Oxford Bulletin of Economics and Statistics, 75, 1, 6479 Bernanke, B. S., Boivin, J., Eliasz, P. (2005). Measuring the effects of monetary policy: A factoraugmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics 120 387422. Boivin, J. and S. Ng (2005). Understanding and comparing factorbased forecasts. International Journal of Central Banking, 1, 3, 117151. Breitung J. and Eickmeier S. (2006). Dynamic factor models. In Hübler O. and Frohn J. (eds.), Modern Econometric Analysis, Chap. 3, Springer. Breitung J. and Pigorsch U. (2012), A canonical correlation approach for selecting the number of dynamic factors, forthcoming in: Oxford Bulletin of Economics and Statistics. Brillinger D. R.(1981), Time series: Data analysis and theory. HoldenDay, Inc., San Francisco. Campbell, J.Y., Lo, A. and MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton University Press, Princeton, N.J. Cattell, R. B. (1966) “The Scree Test for the Number of Factors”, Multivariate Behavioral Research, vol. 1, 24576. Cipollini, A. and Missaglia, G. (2007), "Dynamic Factor analysis of industry sector default rates and implication for Portfolio Credit Risk Modelling," MPRA Paper 3582, University Library of Munich, Germany Connor, G. and Korajcyk, R. (1993), A Test for the Number of Factors in an Approximate Factor Model, Journal of Finance XLVIII:4 , 1263–1291. Cragg, J. and Donald, S. (1997), Inferring the Rank of a Matrix, Journal of Econometrics 76 , 223– 250. Antonello D’ Agostino & Domenico Giannone (2013). "Comparing Alternative Predictors Based on Large‐Panel Factor Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 306326, 04. Donald, S. (1997), Inference Concerning the Number of Factors in a Multivariate Nonparameteric Relationship, Econometrica 65:1 , 103–132. Eickmeier, S., Ziegler C. (2008), How successful are dynamic factor models at forecasting output and inflation? A metaanalytic approach, Journal of Forecasting, 27(3), 237265. Forni, M. and Reichlin, L. (1998), Let’s Get Real: a FactorAnalytic Approach to Disaggregated Business Cycle Dynamics, Review of Economic Studies 65 , 453–473. Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2000), The Generalized Dynamic Factor Model: Identification and Estimation, The Review of Economics and Statistics Vol. 82, No. 4 (Nov., 2000), pp. 540554. Forni, M. & Giannone, D. & Lippi, M. & Reichlin, L, (2009). "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(05), pages 13191347, October. Guttman, L. (1954), Some necessary conditions for commonfactor analysis. Psychometrika, 19, 149161. Hallin M. and Liska R. (2007). Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102, 603617. Koopman S. J. and Van der Wel M. (2013), Forecasting the U.S. Term Structure of Interest Rates Using a Macroeconomic Smooth Dynamic Factor Model, International Journal of Forecasting, Forthcoming. Lewbel, A. (1991), The Rank of Demand Systems: Theory and Nonparameteric Estimation, Econometrica 59 , 711–730. Johnstone, I.M. (2001), “On the Distribution of the Largest Eigenvalue in Principal Component Analysis,” Annals of Statistics, 29, 295327. Kapetanios G. (2004). A new method for determining the number of factors in factor models with large datasets. Working paper No 525, Department of Economics, Queen Mary University of London. Kapetanios G. (2010) . A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets. Journal of Business and Economic Statistics, 28(3), pp. 397409. Kapetanios G. and Marcellino M. (2009). A Parametric Estimation Method for Dynamic Factor Models of Large Dimension. Journal of Time Series Analysis, 30(2), pp. 208238. Jum C. Nunnally, Ira H. Bernstein (1994), Psychometric theory (3rd edition), New York: McGrawHill. Onatski A., (2009). "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 14471479, 09. Onatski A., (2010). "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 10041016, November. Schumacher C. (2007). Forecasting German GDP using alternative factor models based on large datasets, mimeo. Schwert G.W (1989) “Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business and Economic Statistics, 7 (April), 147159. Tracy, C. A.; Widom, H. (1994), "Levelspacing distributions and the Airy kernel", Communications in Mathematical Physics 159 (1): 151–174. Bai, Z. D. and Silverstein, J. W. (1998). No eigenvalues outside the support of the limiting spectral distribution of large dimensional sample covariance matrices. Annals of Probability, 26 , 316345. Wilson P. and Cooper C. (2008), Finding the magic number, The Psychologist, Volume 21, Part 10 and October 2008. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/50005 