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

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  • Lucia Alessi
  • Matteo Barigozzi
  • Marco Capasso

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 multiplying the penalty function times a constant which tunes the penalizing power of the function itself as in the Hallin and Liska [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.

Suggested Citation

  • 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.
  • Handle: RePEc:ssa:lemwps:2007/19
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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. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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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    Cited by:

    1. Juan José Echavarría & Andrés González, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 14-66, Diciembre.
    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. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    4. Le, Vu & Wang, Qing, 2014. "Robust thresholding for Diffusion Index forecast," Economics Letters, Elsevier, vol. 125(1), pages 52-56.
    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, vol. 30(69), pages 195-256, December.
    6. Alain Kabundi & Francisco Nadal De Simone, 2011. "France in the global economy: a structural approximate dynamic factor model analysis," Empirical Economics, Springer, vol. 41(2), pages 311-342, October.
    7. Vedolin, Andrea, 2012. "Uncertainty and leveraged Lucas Trees: the cross section of equilibrium volatility risk premia," LSE Research Online Documents on Economics 43091, London School of Economics and Political Science, LSE Library.
    8. 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.
    9. Siem Jan Koopman & André Lucas & Bernd Schwaab, 2012. "Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 521-532, May.
    10. Gao, Quansheng & Hu, Chengjun, 2009. "Dynamic mortality factor model with conditional heteroskedasticity," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 410-423, December.
    11. 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.
    12. repec:pal:assmgt:v:17:y:2016:i:3:d:10.1057_jam.2016.1 is not listed on IDEAS

    More about this item

    Keywords

    Approximate factor models; Information criterion; Number of Factors;

    JEL classification:

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

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