论文标题
宏观经济预测与分数因子模型
Macroeconomic Forecasting with Fractional Factor Models
论文作者
论文摘要
我们将高维因子模型与分数集成方法相结合，并得出模型，在这些模型中，非组织，潜在协调的数据的不同持久性数据被建模为常见分数整合因子的函数。提出了一个将主要组件和卡尔曼过滤器结合的两阶段估计器。研究了高维的美国宏观经济数据集的预测性能，我们发现从分数因子模型中受益可能是很大的，因为它们优于单变量自动化，主组件和因子设定的错误校正模型。
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.
