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Observation driven mixed-measurement dynamic factor models with an application to credit risk

Author

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  • Schwaab, Bernd
  • Koopman, Siem Jan
  • Lucas, André
  • Creal, Drew

Abstract

We propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common dynamics and cross-sectional dependence due to shared exposure to dynamic latent factors. The distinguishing feature of our model is that the likelihood function is known in closed form and need not be obtained by means of simulation, thus enabling straightforward parameter estimation by standard maximum likelihood. We use the new mixed-measurement framework for the signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody JEL Classification: C32, G32

Suggested Citation

  • Schwaab, Bernd & Koopman, Siem Jan & Lucas, André & Creal, Drew, 2013. "Observation driven mixed-measurement dynamic factor models with an application to credit risk," Working Paper Series 1626, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20131626
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    References listed on IDEAS

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    More about this item

    Keywords

    Default risk; dynamic beta density; dynamic factor model; dynamic ordered probit; loss given default; Panel data;

    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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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