大型数据集中的常见因素、趋势和周期
Common factors, trends, and cycles in large datasets
摘要：
本文考虑了一个大数据集的非平稳动态因子模型，以区分长期和短期的协同运动。首先，我们提出了一个新的准极大似然估计模型的基础上卡尔曼平滑和期望最大化算法。讨论了估计量的渐近性质。然后，我们展示了如何通过估计的非平稳因素的特征分析来分离因素中的趋势和周期。最后，我们使用我们的方法对美国季度宏观经济指标面板估计总实际产出，或国内生产总值，和产出缺口。
英文摘要：
This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross Domestic Output, and the output gap.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
