Empirical Bayes Methods for Dynamic Factor Models
研究了动态因子模型中载荷矩阵和动态因子作为潜在随机过程的经验贝叶斯估计方法，通过蒙特卡洛模拟评估有限样本性质，并应用于美国宏观经济时间序列预测。
We consider the dynamic factor model where the loading matrix, the dynamic factors, and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the shrinkagebased estimation of the loadings and factors. We investigate the methods in a large Monte Carlo study where we evaluate the finite sample properties of the empirical Bayes methods for quadratic loss functions. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.