Information Matrix

In the debate about how fast mortality will improve in the future, sometimes it is useful to remind ourselves how far we have come. 

With the exception of dressmaking, bias is generally undesirable. This is particularly the case when projecting future mortality rates for reserving for pension liabilities. 

A good general-purpose formula for describing pensioner mortality rates is the logistic function: q = exp(α) / (1 + exp(α)) where the value of α varies by age.

An important concept is demography is the ecological fallacy.  This is where aggregate data for a group are used to draw erroneous inferences about individuals belonging to the group. 

Amongst its other claims to fame, Scotland produced one of the earliest prominent anti-smoking campaigners — our very own King James VI was an early opponent of tobacco consumption and smoking

Last year Iain wrote about a smooth model to identify mortality shocks, using Swedish population data to illustrate the impact of the 1918 influenza pandemic. 

One of the most written-about models for stochastic mortality projections is that from Lee & Carter (1992). 

A particular leitmotif of 2010 is productivity — getting more work done with the time and resources available.  Often this is about controlling costs, but in the insurance sector in the European Union it is also about adapting to scarcity of resources: with Solvency II looming, there is strong Europe-wide demand for actuarial expertise.

Stephen has written about the challenges in using population cause-of-death data for mortality analysis and forecasting. Another potential source of data is computerised patient records such as the General Practice Research Database (GPRD).

On 18th May 2010 we ran a seminar on mortality projections for clients, including hands-on use of the Projections Toolkit.