Properly modeling changes over time is essential for forecasting and important for any model or process with data that span multiple time periods. Despite this, most approaches used are ad hoc or lack a statistical framework for making accurate forecasts.
A method is presented to add time series components within a penalized regression framework so that these models are capable of handling everything a penalized generalized linear model can handle (distributional flexibility and credibility) as well as changes over time. Doing this, a subset of state space model functionality can be incorporated in a more familiar framework. The benefits of state space models in terms of their accuracy and intuitiveness are explained.
The method presented here lends itself well to presentation, which can help with understanding and delivering results. This makes it useful not only for pricing and other models but for improving and streamlining other types of actuarial processes such as reserving and profitability studies.
