Cohort extensions of the Poisson common factor model for modelling both genders jointly

The earlier work on mortality modelling and forecasting has largely focused on the study of a single population. Recently, there is an emerging strand of literature that emphasises the interrelationship between multiple populations. In this paper, we examine some cohort extensions of the Poisson common factor model for modelling both genders jointly. The cohort effect is specified in six alternatives which are applied to data-sets from five developed regions. We find that direct parameterisation of cohort effect could improve model fitting, reduce the need for additional period factors, and produce consistent mortality forecasts for females and males. Furthermore, we find that the cohort effect appears to be gender indifferent for the populations examined and has an interaction effect with age in certain cases.     

Small-Sample Forecasting Regression or Arima Models?

Univariate models offer the most convenient options for forecasting and ARIMA models are still the most popular among them. The ARIMA modelling, however, requires long data series. This paper shows that a regression model may be estimated with a far greater efficiency in very small samples compared to the corresponding ARIMA model. As a result the larger information set used in a regression model may compensate for the small sample size and improve the forecast efficiency substantially. Three applications which utilize autoregressive forecasts on the exogenous variables highlight the gains in forecast efficiency in small samples from regressions over the ARIMA models.