Model error and its estimation, with particular application to loss reserving

TL;DR

This paper develops a Bayesian approach using LASSO for estimating model error in loss reserving, addressing internal model error and providing a framework for quantifying forecast uncertainty in insurance.

Contribution

It introduces a Bayesian model averaging method with LASSO to estimate internal model error and combines bootstrapping to improve model support, advancing loss reserving accuracy.

Findings

Bayesian model averaging with LASSO effectively estimates internal model error.

Bootstrapping enhances the set of admissible models for better uncertainty quantification.

Parameter and model errors are intertwined, complicating their separation in forecasts.

Abstract

This paper is concerned with forecast error, particularly in relation to loss reserving. This is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and the likelihood of observed data. A posterior on the model set, conditional on the data, results, and an estimate of model error (contained in a loss reserve) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be thinner than desired, and bootstrapping of the LASSO is used to gain bulk. This provides the bonus of an estimate of parameter error also. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.