摘要翻译：
我们将高维因子模型与分数积分方法相结合，并推导出模型，其中不同持久性的非平稳、潜在协整数据被建模为公共分数积分因子的函数。提出了一种结合主成分和Kalman滤波的两阶段估计器。对高维美国宏观经济数据集的预测性能进行了研究，我们发现分数因子模型的收益可以很大，因为它们优于单变量自回归、主成分和因子扩大误差修正模型。
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英文标题：
《Macroeconomic Forecasting with Fractional Factor Models》
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作者：
Tobias Hartl
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented error-correction model.
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PDF链接：
https://arxiv.org/pdf/2005.04897