Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
AbstractThis paper has been accepted for publication in the 'Review of Economics and Statistics'. 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.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-042/2/DSF16.
Date of creation: 21 Feb 2011
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panel data; loss given default; default risk; dynamic beta density; dynamic ordered probit; dynamic factor model;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-03-26 (All new papers)
- NEP-BAN-2011-03-26 (Banking)
- NEP-ECM-2011-03-26 (Econometrics)
- NEP-RMG-2011-03-26 (Risk Management)
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- Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2012. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," Tinbergen Institute Discussion Papers 12-020/4, Tinbergen Institute.
- Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2012. "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes," Tinbergen Institute Discussion Papers 12-059/4, Tinbergen Institute.
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