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

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

Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This paper led to a publication in the 'Review of Economics and Statistics' , 2014, 96(5), 898-915.

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: panel data; loss given default; default risk; dynamic beta density; dynamic ordered probit; dynamic factor model (search for similar items in EconPapers)
JEL-codes: C32 G32 (search for similar items in EconPapers)
Date: 2011-02-21
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk (2014)
Working Paper: Observation driven mixed-measurement dynamic factor models with an application to credit risk (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20110042

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