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A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models

Author

Listed:
  • Catherine Doz

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Domenico Giannone

    (ECARES - European Center for Advanced Research in Economics and Statistics - ULB - Université libre de Bruxelles = Free University of Brussels, CEPR - Center for Economic Policy Research)

  • Lucrezia Reichlin

    (CEPR - Center for Economic Policy Research, London Business School - London Business School)

Abstract

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T) going to in¯nity along any path and that maximum likelihood is viable for n large. The estimator is robust to misspecification of cross-sectional and time series correlation of the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.

Suggested Citation

  • Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," Post-Print hal-00638440, HAL.
    • Handle: RePEc:hal:journl:hal-00638440
    DOI: 10.1162/REST_a_00225
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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