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A robust criterion for determining the number of static factors in approximate factor models

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  • Alessi, Lucia
  • Barigozzi, Matteo
  • Capasso, Marco

Abstract

We propose a refinement of the criterion by Bai and Ng [2002] for determining the number of static factors in factor models with large datasets. It consists in multi-plying the penalty function by a constant which tunes the penalizing power of the function itself as in the Hallin and Liška [2007] criterion for the number of dynamic factors. By iteratively evaluating the criterion for different values of this constant, we achieve more robust results than in the case of fixed penalty function. This is shown by means of Monte Carlo simulations on seven data generating processes, including heteroskedastic processes, on samples of different size. Two empirical applications are carried out on a macroeconomic and a financial dataset. JEL Classification: C52

Suggested Citation

  • Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2008. "A robust criterion for determining the number of static factors in approximate factor models," Working Paper Series 903, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2008903
    Note: 1023254
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    References listed on IDEAS

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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. 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.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. 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(5), pages 1319-1347, October.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series," LEM Papers Series 2006/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
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    3. 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.
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    8. Alain Kabundi & Tumisang Loate & Nicola Viegi, 2020. "Spillovers of the Conventional and Unconventional Monetary Policy from the US to South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 88(4), pages 435-471, December.
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    11. Matteo Barigozzi & Marco Capasso, 2007. "A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance," LEM Papers Series 2007/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
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    14. Enrique López E. & Fernando Tenjo Galarza & Diego H. Rodríguez H., 2012. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 30(69), pages 195-256, December.
    15. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    16. Dzhamilya Abuzyarova & Veronika Belousova & Zhaklin Krayushkina & Yulia Lonshcikova & Ekaterina Nikiforova & Nikolay Chichkanov, 2019. "The Role of Human Capital in Science, Technology and Innovation," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 13(2), pages 107-119.
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    18. Enrique López E. & Fernando Tenjo Galarza & Diego H. Rodríguez H., 2012. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 30(69), pages 195-256, December.
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    More about this item

    Keywords

    Approximate factor models; Information criterion; Number of factors.;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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