Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk
提出一种观测驱动的动态因子模型，处理混合测量和混合频率的面板数据，似然函数闭合可用标准最大似然估计，应用于美国穆迪评级公司的宏观、信用和违约损失风险信号提取与预测。
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.