We examine the problem of making reconciled forecasts of large collections of
related time series through a behavioural/Bayesian lens. Our approach
explicitly acknowledges and exploits the 'connectedness' of the series in terms
of time-series characteristics and forecast accuracy as well as hierarchical
structure. By making maximal use of the available information, and by
significantly reducing the dimensionality of the hierarchical forecasting
problem, we show how to improve the accuracy of the reconciled forecasts. In
contrast to existing approaches, our structure allows the analysis and
assessment of the forecast value added at each hierarchical level. Our
reconciled forecasts are inherently probabilistic, whether probabilistic base
forecasts are used or not