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

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

No 1626, Working Paper Series from European Central Bank

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

Keywords: Default risk; dynamic beta density; dynamic factor model; dynamic ordered probit; loss given default; Panel data (search for similar items in EconPapers)
Date: 2013-12
New Economics Papers: this item is included in nep-ban and nep-rmg
Note: 955417
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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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 (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20131626

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