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Signal or noise?

Each year since 2009 the CMI in the UK has released a spreadsheet tool for actuaries to use for mortality projections. I have written about this tool a number of times, including how one might go about setting the long-term rate. The CMI now wants to change how the spreadsheet is calibrated and has proposed the following model in CMI (2016a):

\[\log m_{x,y} = \alpha_x + \beta_x(y-\bar y) + \kappa_y + \gamma_{y-x}\qquad (1)\]

25 November 2016 Written by: Stephen RichardsTags: CMI, APCI, APC, Lee-Carter, Age-Period, smoothing

Excel's limits

We have written in the past about some of the reasons why we don't use Excel to fit our models. However, we do use Excel for validation purposes — fitting models using two entirely separate tools is a good way of checking production code. That said, there are some important limits to Excel, especially when it comes to fitting projection models.

27 June 2014 Written by: Stephen RichardsTags: Excel, Lee-Carter, APC, Cairns-Blake-Dowd

Demography's dark matter: measuring cohort effects

My last blog generated quite a bit of interest so I thought I'd write again on cohorts. It's easy to (a) demonstrate the existence of a cohort effect and to (b) fit models with cohort terms, but not so easy to (c) interpret or forecast the fitted cohort coefficients. In this blog I'll fit the following three models:

19 May 2014 Written by: Iain CurrieTags: cohort effect, APC, mortality projections

Forecasting with cohorts for a mature closed portfolio

At a previous seminar I discussed forecasting with the age-period-cohort (APC) model:

$$ \log \mu_{i,j} = \alpha_i + \kappa_j + \gamma_{j-i}$$

27 March 2014 Written by: Iain CurrieTags: APC, mortality projections, cohort effect