We consider whether imposing long-run restrictions on survey respondents’ long-horizon forecasts will enhance their accuracy. The restrictions are motivated by the belief that the macro-variables consumption, investment and output move together in the long run, and that this should be evident...

The effects of data uncertainty on real-time decision-making can be reduced by predicting early revisions to US GDP growth. We show that survey forecasts efficiently anticipate the first-revised estimate of GDP, but that forecasting models incorporating monthly economic indicators and daily...

We show that factor forecasting models deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one...

Application of the Bernhardt, Campello and Kutsoati (2006) test of herding to the calendar-year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon when they tend to herd. We consider whether these types of...

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested,...

We find that model estimates of the term structure of ex ante or perceived macro uncertainty are more in line with realized uncertainty than survey respondents’ perceptions for both inflation and output growth. Survey estimates contain short-term vari- ation in short-horizon uncertainty which...