Information Matrix

A phenomenon to watch for is that of the "self-prophesying model".  It occurs when a variable is too specific to the mortality experience of a reference portfolio to have wider application. 

A tool often used by demographers is the distribution of age at death in a population.  This is known to actuaries as the curve of deaths, and the past 170 years have seen a rather remarkable transformation in this curve. 

 Every good statistician knows that correlation does not imply causation.  Just because two things appear linked does not mean they are. However, with historical data we often don't have the luxury of carrying out controlled, scientific experiments to see if A really does cause B.

Liabilities within any given portfolio are rarely equal, and they usually differ widely in size. Typically, a large proportion of liabilities is concentrated in a relatively small number of lives, so this should always be checked.

Public-health officials typically allocate their resources using evidence-based methods. They know their annual budget for spending on health measures, and they typically want to save as many lives as they can with that fixed budget.

There remains some residual apprehension around open source software (OSS), despite the fact it is increasingly widely adopted.

During an analysis of a large annuity portfolio we took some time out to look at the pattern of mortality by season as well as the overall time trend. We fitted a model for age, gender and season, where the definition of season is that used by the ONS: each season covers three months, and where winter covers December, January and February.

In any model-fitting exercise you will be faced with choices. What shape of mortality curve to use? Which risk factors to include? How many size bands for benefit amount? In each case there is a balance to be struck between improving the model fit and making the model more complicated.

We discussed how we use the AIC to choose between models.