  This expository paper discusses Bayesian decision analysis perspectives on
problems of constrained forecasting. Foundational and pedagogic discussion
contrasts decision analytic approaches with the traditional, but typically
inappropriate, inferential approach. Illustrative examples include development
of novel constrained point forecasting and entropic tilting methodology to
explore consistency of a predictive distribution with an imposed or
hypothesized constraint. Linear, aggregate constraints define illuminating
examples that relate to broadly important problems involving aggregate and
hierarchical constraints in commercial and economic forecasting. Discussion
explores the impact of different loss functions, questions of how constrained
forecasting is impacted by dependencies among outcomes being predicted, and
promotes the broader use of decision analysis including routine evaluation of
predictive distributions of loss under chosen forecasts/decisions. Extensions
to more general constrained forecasting problems, connections with broader
interests in forecast reconciliation and other considerations are noted.
