  In mortality modelling, cohort effects are often taken into consideration as
they add insights about variations in mortality across different generations.
Statistically speaking, models such as the Renshaw-Haberman model may provide a
better fit to historical data compared to their counterparts that incorporate
no cohort effects. However, when such models are estimated using an iterative
maximum likelihood method in which parameters are updated one at a time,
convergence is typically slow and may not even be reached within a reasonably
established maximum number of iterations. Among others, the slow convergence
problem hinders the study of parameter uncertainty through bootstrapping
methods. In this paper, we propose an intuitive estimation method that
minimizes the sum of squared errors between actual and fitted log central death
rates. The complications arising from the incorporation of cohort effects are
overcome by formulating part of the optimization as a principal component
analysis with missing values. Using mortality data from various populations, we
demonstrate that our proposed method produces satisfactory estimation results
and is significantly more efficient compared to the traditional
likelihood-based approach.
