期刊名称：Euro Area Balance of Payments and International Investment Position Statistics
印刷版ISSN：1830-3420
电子版ISSN：1830-3439
出版年度：2013
出版社：European Central Bank
摘要：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. Our joint modeling framework allows us to construct predictive (conditional) loss densities for portfolios of corporate bonds in the presence of different sources of credit risk such as frailty effects and systematic recovery risk.
关键词：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. Our joint modeling framework allows us to;construct predictive (conditional) loss densities for portfolios of corporate bonds in the;presence of different sources of credit risk such as frailty effects and systematic recovery;risk.
