A Single Factor Model for Constructing Dynamic Life Tables

The objective of this paper is to develop a single factor model to construct dynamic life tables. The paper seeks to identify the mortality rate that best explains the global behavior of life tables. Once this key rate is identified, we assume that changes in mortality rates depend linearly on changes in the mortality rate corresponding to the key rate. Next, we proceed to adjust the sensitivities of the changes in mortality rates to changes in the key mortality rate, using non-parametric methods. Assuming that this rate follows a specific ARIMA process it can be used to forecast future mortality rates. The resulting model has a similar structure to the well-known Lee-Carter model but with the advantage that their parameters and variables can be easily identified. Finally, the forecasting ability of the model is tested using out-of-sample data from Spanish experience. The results show that the proposed Single Factor Model significantly outperforms the Lee-Carter model.