论文标题
用马尔可夫开关因子模型对大维数据集进行建模
Modelling Large Dimensional Datasets with Markov Switching Factor Models
论文作者
论文摘要
我们研究了一个新型的大维近似因子模型，其负载的变化是由潜在的一阶马尔可夫工艺驱动的。通过利用模型的等效线性表示，我们首先通过主成分分析恢复潜在因素。然后，我们以状态空间形式施放模型，并根据Baum-Lindgren-Hamilton-kim滤波器的修改版本通过EM算法来估算负载和过渡概率，并使用先前估计的因素来估算。我们的方法很有吸引力，因为它为所有估计器提供了封闭的表达式。更重要的是，它不需要了解真实因素的知识。我们得出了提出的估计程序的理论特性，并通过一组蒙特卡洛实验表明了它们良好的有限样本性能。通过在美国大型股票收益，宏观经济变量和通货膨胀指数的大型美国数据集的应用中，我们的方法的经验实用性得到了说明。
We study a novel large dimensional approximate factor model with regime changes in the loadings driven by a latent first order Markov process. By exploiting the equivalent linear representation of the model, we first recover the latent factors by means of Principal Component Analysis. We then cast the model in state-space form, and we estimate loadings and transition probabilities through an EM algorithm based on a modified version of the Baum-Lindgren-Hamilton-Kim filter and smoother that makes use of the factors previously estimated. Our approach is appealing as it provides closed form expressions for all estimators. More importantly, it does not require knowledge of the true number of factors. We derive the theoretical properties of the proposed estimation procedure, and we show their good finite sample performance through a comprehensive set of Monte Carlo experiments. The empirical usefulness of our approach is illustrated through three applications to large U.S. datasets of stock returns, macroeconomic variables, and inflation indexes.
