MAXIMUM LIKELIHOOD ESTIMATION OF FACTOR MODELS ON DATASETS WITH ARBITRARY PATTERN OF MISSING DATA
修改期望最大化算法，使其能在任意缺失数据模式下估计动态因子模型参数，并处理序列相关的异质成分，适用于不同发布延迟、频率和样本长度的指标集，在欧元区GDP即时预测中验证了有效性。
SUMMARY In this paper we modify the expectation maximization algorithm in order to estimate the parameters of the dynamic factor model on a dataset with an arbitrary pattern of missing data. We also extend the model to the case with a serially correlated idiosyncratic component. The framework allows us to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant, for example, for young economies for which many indicators have been compiled only recently. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting of the euro area gross domestic product. Copyright © 2012 John Wiley & Sons, Ltd.