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A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models

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Author Info
Catherine Doz
Domenico Giannone
Lucrezia Reichlin

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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 infinity along any path of n and T and that therefore maximum likelihood is viable for n large. The estimator is robust to misspecification of the cross-sectional and time series correlation of the the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.

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Paper provided by Université Libre de Bruxelles, Ecares in its series ECARES Working Papers with number 2008_034.

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Length: 28 pages
Date of creation: 2008
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Handle: RePEc:eca:wpaper:2008_034

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Related research
Keywords: Factor Model; large cross-sections; Quasi Maximum Likelihood;

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Find related papers by JEL classification:
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2008. "A review of nonfundamentalness and identification in structural VAR models," Working Paper Series 922, European Central Bank. [Downloadable!]
    Other versions:
  2. Lasse Bork & Hans Dewachter & Romain Houssa, 2009. "Identification of Macroeconomic Factors in Large Panels," CREATES Research Papers 2009-43, School of Economics and Management, University of Aarhus. [Downloadable!]
  3. Matteo Ciccarelli & Benoît Mojon, 2005. "Global inflation," Working Paper Series 537, European Central Bank. [Downloadable!]
    Other versions:
  4. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute. [Downloadable!]
    Other versions:
  5. David F. Hendry & Kirstin Hubrich, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank. [Downloadable!]
    Other versions:
  6. Domenico Giannone & Lucrezia Reichlin & David Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
    Other versions:
  7. D'Agostino, A & Surico, P, 2007. "Does global liquidity help to forecast US inflation?," MPRA Paper 6283, University Library of Munich, Germany. [Downloadable!]
    Other versions:
  8. Hwee Kwan Chow & Keen Meng Choy, 2008. "Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Economic Growth centre Working Paper Series 0802, Nanyang Technolgical University, School of Humanities and Social Sciences, Economic Growth centre. [Downloadable!]
  9. Marco Del Negro & Christopher Otrok, 2008. "Dynamic factor models with time-varying parameters: measuring changes in international business cycles," Staff Reports 326, Federal Reserve Bank of New York. [Downloadable!]
  10. Marta Banbura & Gerhard Rünstler, 2007. "A look into the factor model black box - publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 751, European Central Bank. [Downloadable!]
  11. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  12. Borus Jungbacker & Siem Jan Koopman, 2008. "Likelihood-based Analysis for Dynamic Factor Models," Tinbergen Institute Discussion Papers 08-007/4, Tinbergen Institute. [Downloadable!]
  13. Gregor Bäurle, 2008. "Priors from DSGE Models for Dynamic Factor Analysis," Diskussionsschriften dp0803, Universitaet Bern, Departement Volkswirtschaft. [Downloadable!]
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