The short answer for mortality work is that your Poisson model is never truly Poisson. The longer answer is that the true distribution has a similar likelihood, so you will get the same answer from treating it like Poisson.  Your model is pseudo-Poisson, but not actually Poisson.

Last week I presented at the Longevity 18 conference.  My topic was on robustifying stochastic mortality models when the calibrating data contain outliers, such as caused by the COVID-19 pandemic.  A copy of the presentation can be downloaded here, which is based on a paper to be presented at an IFoA sessional meeting in N

The Institute and Faculty of Actuaries (IFoA) is hosting a meeting on robust mortality forecasting in the presence of outliers on 27th November 2023.  A copy of the draft paper and booking details for attendance can be found on the IFoA website.

2.8.7 update

• A new resource area Training tab including datasets and exercises.
• Availability of 2D Period shocks model as per Kirkby & Currie (2010).
• Availability of a CBD M9 model with smoothing on main age term.
• New plot shows the inverse roots of the characteristic polynomials of the autoregressive (AR) and moving-average (MA) parts of the ARIMA model.
• License configurable SAML-based single-sign-on authentication.
• Platform, security and administrative support updates.
• Various fix

in Kleinow & Richards (2016, Table 5) we noted a seeming conundrum: the best-fitting ARIMA model for the time index in a Lee-Carter model also produced much higher value-at-risk (VaR) capital requirements for longevity trend risk.  How could this be?

The late Iain Currie was a long-time advocate of smoothing certain parameters in mortality models.  In an earlier blog he showed how smoothing parameters in the Lee-Carter model could improve the quality of the forecast.  As Iain himself wrote, "this idea is not new" and traced its origins to Delwarde, Denuit & Eilers (2007).

The covid-19 pandemic caused mortality shocks in many countries, and these shocks severely impact the standard forecasting models used by actuaries.  I previously showed how to robustify time-series models with a univariate index (Lee-Carter, APC) and those with a multivariate index (Cairns-Blake-Dowd, Ta

In earlier blogs I discussed two techniques for handling outliers in mortality forecasting models:

• Chen & Liu (1993) for ARIMA models for univariate time series, and

In mortality forecasting work we often deal with downward trends.  It is often tempting to jump to the assumption of a linear trend, in part because this makes for easier mathematics.  However, real-world phenomena are rarely purely linear, and the late Iain Currie advocated linear adjustment as means of judging linear-seeming patterns.  This involves calculating a line between the first and last points, and deducting the line value at ea