  This paper considers forecasts of the growth and inflation distributions of
the United Kingdom with factor-augmented quantile autoregressions under a model
averaging framework. We investigate model combinations across models using
weights that minimise the Akaike Information Criterion (AIC), the Bayesian
Information Criterion (BIC), the Quantile Regression Information Criterion
(QRIC) as well as the leave-one-out cross validation criterion. The unobserved
factors are estimated by principal components of a large panel with N
predictors over T periods under a recursive estimation scheme. We apply the
aforementioned methods to the UK GDP growth and CPI inflation rate. We find
that, on average, for GDP growth, in terms of coverage and final prediction
error, the equal weights or the weights obtained by the AIC and BIC perform
equally well but are outperformed by the QRIC and the Jackknife approach on the
majority of the quantiles of interest. In contrast, the naive QAR(1) model of
inflation outperforms all model averaging methodologies.
