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DYNAMIC PRINCIPAL COMPONENT REGRESSION: APPLICATION TO AGE-SPECIFIC MORTALITY FORECASTING

Published online by Cambridge University Press:  20 June 2019

Han Lin Shang Open the ORCID record for Han Lin Shang [Opens in a new window]
Han Lin Shang*
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, Level 4, Building 26CAustralian National University Kingsley Street, Acton, Canberra ACT 2601, Australia
*
E-Mail: hanlin.shang@anu.edu.au.
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Abstract

In areas of application, including actuarial science and demography, it is increasingly common to consider a time series of curves; an example of this is age-specific mortality rates observed over a period of years. Given that age can be treated as a discrete or continuous variable, a dimension reduction technique, such as principal component analysis (PCA), is often implemented. However, in the presence of moderate-to-strong temporal dependence, static PCA commonly used for analyzing independent and identically distributed data may not be adequate. As an alternative, we consider a dynamic principal component approach to model temporal dependence in a time series of curves. Inspired by Brillinger’s (1974, Time Series: Data Analysis and Theory. New York: Holt, Rinehart and Winston) theory of dynamic principal components, we introduce a dynamic PCA, which is based on eigen decomposition of estimated long-run covariance. Through a series of empirical applications, we demonstrate the potential improvement of 1-year-ahead point and interval forecast accuracies that the dynamic principal component regression entails when compared with the static counterpart.

Keywords

Dimension reductionfunctional time seriesKernel sandwich estimatorlong-run covariancemultivariate time series

Information

Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 49 , Issue 3 , September 2019 , pp. 619 - 645
Copyright
© Astin Bulletin 2019 

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