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

by Drew Creal of the University of Chicago,
Bernd Schwaab of the European Central Bank,
Siem Jan Koopman of the VU University Amsterdam & Tinbergen Institute, Amsterdam, and
André Lucas of the European Central Bank & Tinbergen Institute, Amsterdam

February 11, 2011

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's-rated firms from January 1982 until March 2010.

JEL Classification: C32, G32.

Keywords: panel data, loss given default, default risk, dynamic beta density, dynamic ordered probit, dynamic factor model

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