||Mortality forecasting plays an essential role in designing welfare policies and pricing aged-related financial derivatives. However, most prevailing models do not perform well in mortality forecasting particularly for the elder people. Indeed, the missing mortality data for the elder people is a typical feature in developing countries, because people are shorter-lived in earlier times and hence the mortality is recorded at fewer age categories. For example, in Taiwan, the mortality is recorded up to an age of 95 before 1997, but the mortality is recorded up to an age of 100 afterwards. This paper proposes several methods to augment the missing mortality data based on two famous models: the Lee-Carter model (Lee and Carter, 1992) and the Age-Period-Cohort model (Renshaw and Haberman, 2006). Both simulation and empirical studies demonstrate the improvement in terms of out-of-sample forecasting using a suitable data augmentation technique.|
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