OBSERVATION-DRIVEN MIXED-MEASUREMENT DYNAMIC FACTOR MODELS WITH AN APPLICATION TO CREDIT RISK

• 成果类型: Article
• 署名作者: Creal, Drew; Schwaab, Bernd; Koopman, Siem Jan; Lucas, Andre
• 署名单位: University of Chicago; European Central Bank; Vrije Universiteit Amsterdam; Tinbergen Institute
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00393
• 发表日期: 2014-12
• 页码: 898-915
• 关键词: time-series; number; volatility; cycles; return

摘要

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.