论文标题
功能时间序列的近似因素模型
Approximate Factor Models for Functional Time Series
论文作者
论文摘要
我们提出了一种用于分析时间依赖性曲线数据的新型近似因子模型。我们的模型将这些数据分解为两个不同的组件：低维可预测因子成分和不可预测的误差项。这些组件是通过基础功能时间序列的自相关结构来识别的。始终使用累积自动配置运算符的特征组件来估算模型参数，并提出了信息标准以确定适当的因素数量。在死亡率和产量曲线建模上的应用说明了我们方法比广泛使用的功能主成分分析的关键优势，因为它提供了基础动力学的简约结构表示以及样本外预测性能的增长。
We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information criterion is proposed to determine the appropriate number of factors. Applications to mortality and yield curve modeling illustrate key advantages of our approach over the widely used functional principal component analysis, as it offers parsimonious structural representations of the underlying dynamics along with gains in out-of-sample forecast performance.
