Parameter Reduction in Actuarial Triangle Models

Abstract

Very similar modeling is done for actuarial models in loss reserving and mortality projection. Both start with incomplete data rectangles, traditionally called triangles, and model by year of origin, year of observation, and lag from origin to observation. Actuaries using these models almost always use some form of parameter reduction as there are too many parameters to fit reliably, but usually this is an ad hoc exercise. Here we try two formal statistical approaches to parameter reduction, random effects and Lasso, and discuss methods of comparing goodness of fit.