Econ Papers Daily / 期刊论文
函数型时间序列的近似因子模型
Approximate Factor Models for Functional Time Series
摘要
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.
中文摘要
我们提出了一种新颖的近似因子模型，专为分析时间依赖的曲线数据而设计。该模型将此类数据分解为两个不同的组成部分：一个低维可预测的因子成分和一个不可预测的误差项。这些成分通过底层函数型时间序列的自协方差结构加以识别。模型参数利用累积自协方差算子的特征成分进行一致估计，并提出了一种信息准则以确定合适的因子数量。在死亡率建模和收益率曲线建模中的应用表明，我们的方法相较于广泛使用的函数型主成分分析具有关键优势，因为它提供了对底层动态的简约结构表示，并在样本外预测性能上有所提升。