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Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Author

Listed:
  • Drew Creal

    (Booth School of Business, University of Chicago)

  • Bernd Schwaab

    (European Central Bank)

  • Siem Jan Koopman

    (VU University Amsterdam and Tinbergen Institute)

  • Andr� Lucas

    (VU University Amsterdam and Duisenberg School of Finance, and Tinbergen Institute)

Abstract

We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter estimation using standard maximum likelihood methods. We adopt the new framework for signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 to March 2010.

Suggested Citation

  • Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    • Handle: RePEc:tpr:restat:v:96:y:2014:i:5:p:898-915
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    dynamic factor models; forecast accuracy; credit risk;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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