Dynamic principal component regression : application to age-specific mortality forecasting

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      <subfield code="a">Dynamic principal component regression</subfield>
      <subfield code="b">: application to age-specific mortality forecasting</subfield>
      <subfield code="c">Han Lin Shang</subfield>
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      <subfield code="a">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.</subfield>
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      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
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      <subfield code="g">02/09/2019 Volumen 49 Número 3 - septiembre 2019 , p.619-645</subfield>
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