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

Drew Creal, Bernd Schwaab, Siem Jan Koopman and Andre Lucas

The Review of Economics and Statistics, 2014, vol. 96, issue 5, 898-915

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.

Keywords: dynamic factor models; forecast accuracy; credit risk (search for similar items in EconPapers)
JEL-codes: C13 C33 C53 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (80)

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Working Paper: Observation driven mixed-measurement dynamic factor models with an application to credit risk (2013)
Working Paper: Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk (2011)
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