Skip Nav Destination
1-1 of 1
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Publisher: Journals Gateway
The Review of Economics and Statistics (2014) 96 (5): 898–915.
Published: 01 December 2014
AbstractView article PDF
We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter estimation using standard maximum likelihood methods. We adopt the new framework for signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 to March 2010.
Includes: Supplementary data