Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
| Autor: | Drew Creal, Siem Jan Koopman, Andre Lucas, Bernd Schwaab |
|---|---|
| Přispěvatelé: | Econometrics and Operations Research, Finance, A-LAB, Tinbergen Institute, Econometrics and Data Science |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Economics and Econometrics
panel data loss given default default risk dynamic beta density dynamic ordered probit dynamic factor model Estimation theory Computer science jel:C53 jel:C13 Default risk dynamic beta density dynamic factor model dynamic ordered probit loss given default Panel data jel:C32 jel:C33 Loss given default jel:G32 Dynamic factor Econometrics Range (statistics) dynamic factor models forecast accuracy credit risk Macro Likelihood function Social Sciences (miscellaneous) Panel data Credit risk |
| Zdroj: | Review of Economics and Statistics, 96(5), 898-915. MIT Press Journals Creal, D, Schwaab, B, Koopman, S J & André, L 2014, ' Observation driven mixed-measurement dynamic factor models with an application to credit risk ', Review of Economics and Statistics, vol. 96, no. 5, pp. 898-915 . https://doi.org/10.1162/REST_a_00393 Creal, D D, Schwaab, B, Koopman, S J & Lucas, A 2014, ' Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk ', Review of Economics and Statistics, vol. 96, no. 5, pp. 898-915 . https://doi.org/10.1162/REST_a_00393 |
| ISSN: | 0034-6535 |
| DOI: | 10.1162/REST_a_00393 |
| Popis: | This 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. |
| Databáze: | OpenAIRE |
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