Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
Drew D. Creal
University of Chicago - Booth School of Business - Econometrics and Statistics
European Central Bank (ECB) - Directorate General Research
Siem Jan Koopman
VU University Amsterdam; Tinbergen Institute
VU University Amsterdam - Faculty of Economics and Business; Tinbergen Institute
February 11, 2011
Tinbergen Institute Discussion Paper 11-042/DSF 16
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.
Number of Pages in PDF File: 43
Keywords: panel data, loss given default, default risk, dynamic beta density, dynamic ordered probit, dynamic factor model
JEL Classification: C32, G32
Date posted: February 22, 2011
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