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A New Method for Determining the Number of Factors in Factor Models with Large Datasets

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  • George Kapetanios

    (Queen Mary, University of London)

Abstract

The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modelled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modelling. This paper provides an alternative method to information criteria as tools for estimating the number of factors in large dimensional factor models. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favourable for the new method and suggest that it is a reasonable alternative to existing methods.

Suggested Citation

  • 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.
    • Handle: RePEc:qmw:qmwecw:525
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    References listed on IDEAS

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    1. Olivier Jean Blanchard & Stanley Fischer (ed.), 1991. "NBER Macroeconomics Annual 1991," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262521652, December.
    2. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
    3. Lewbel, Arthur, 1991. "The Rank of Demand Systems: Theory and Nonparametric Estimation," Econometrica, Econometric Society, vol. 59(3), pages 711-730, May.
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    Cited by:

    1. 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.
    2. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    3. Simon Freyaldenhoven, 2017. "A Generalized Factor Model with Local Factors," 2017 Papers pfr361, Job Market Papers.
    4. Xu Han & Mehmet Caner, 2017. "Determining the number of factors with potentially strong within-block correlations in error terms," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 946-969, October.
    5. Francesco Moscone & Elisa Tosetti, 2009. "A Review And Comparison Of Tests Of Cross‐Section Independence In Panels," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 528-561, July.
    6. Andrés García-Medina & Graciela González Farías, 2020. "Transfer entropy as a variable selection methodology of cryptocurrencies in the framework of a high dimensional predictive model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-31, January.
    7. Alexander Chudik & M. Hashem Pesaran, 2013. "Large Panel Data Models with Cross-Sectional Dependence: A Survey," CESifo Working Paper Series 4371, CESifo.
    8. Jushan Bai & Shuzhong Shi, 2011. "Estimating High Dimensional Covariance Matrices and its Applications," Annals of Economics and Finance, Society for AEF, vol. 12(2), pages 199-215, November.
    9. Ghate, Chetan & Wright, Stephen, 2012. "The “V-factor”: Distribution, timing and correlates of the great Indian growth turnaround," Journal of Development Economics, Elsevier, vol. 99(1), pages 58-67.
    10. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    11. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
    12. Jean-Philippe Bouchaud & Laurent Laloux & M. Augusta Miceli & Marc Potters, 2005. "Large dimension forecasting models and random singular value spectra," Science & Finance (CFM) working paper archive 500066, Science & Finance, Capital Fund Management.
    13. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2007. "Automatic Leading Indicators (ALIs) versus Macro Econometric Structural Models (MESMs): Comparison of Inflation and GDP growth Forecasting," EcoMod2007 23900072, EcoMod.
    14. Joel Bun & Jean-Philippe Bouchaud & Marc Potters, 2016. "Cleaning large correlation matrices: tools from random matrix theory," Papers 1610.08104, arXiv.org.
    15. Otter, Pieter W. & Jacobs, Jan P.A.M., 2006. "On information in static and dynamic factor models," CCSO Working Papers 200605, University of Groningen, CCSO Centre for Economic Research.
    16. Silvia S.W. Lui, 2006. "An Empirical Study of Asian Stock Volatility Using Stochastic Volatility Factor Model: Factor Analysis and Forecasting," Working Papers 581, Queen Mary University of London, School of Economics and Finance.
    17. Silvia S.W. Lui, 2006. "An Empirical Study of Asian Stock Volatility Using Stochastic Volatility Factor Model: Factor Analysis and Forecasting," Working Papers 581, Queen Mary University of London, School of Economics and Finance.
    18. J.-P. Bouchaud & L. Laloux & M. A. Miceli & M. Potters, 2007. "Large dimension forecasting models and random singular value spectra," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 201-207, January.
    19. Xin Shi & Robert Qiu, 2019. "Estimation of high-dimensional factor models and its application in power data analysis," Papers 1905.02061, arXiv.org, revised Oct 2019.

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    More about this item

    Keywords

    Factor models; Large sample covariance matrix; Maximum eigenvalue;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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