Information Matrix
Filter Information matrix
Posts feedMortality forecasting in a post-COVID world
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 November 20
Robust mortality forecasting for 2D age-period models
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, Tang-Li
M is for Estimation
In earlier blogs I discussed two techniques for handling outliers in mortality forecasting models:
Robust mortality forecasting for multivariate models
In my previous blog I showed how univariate stochastic mortality models like the Lee-Carter and APC models can be robustified to cope with data affected by the covid-19 pandemic. This blog considers multivariate models.
Robust mortality forecasting for univariate models
The covid-19 pandemic led to high levels of mortality in many countries in 2020. How can univariate projections robustly handle such shocks in population data?