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Approximate Factor Models for Functional Time Series
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DOI：10.1080/07350015.2026.2736042.png)
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Abstract
En 中文
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.
Keywords:
Curve data
functional data analysis
identification
information criterion
forecasting
yield curve modeling
C32
C38
C55
E43
Journal
J
IF:
2.5
Papers:
119
Citations:
0
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