The Review of Economics and Statistics Vol. 96 No. 5 2014

Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Drew Creal¹; Bernd Schwaab²; Siem Jan Koopman³; André Lucas³

¹ University of Chicago · ² European Central Bank · ³ Tinbergen Institute

Abstract

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.

DOI

10.1162/rest_a_00393

Volume

96

Issue

5

Pages

898-915

Language

en

Sources

openalex crossref