  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.
