Showing 1 - 10 of 351
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,...
Persistent link: https://www.econbiz.de/10011210423
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...
Persistent link: https://www.econbiz.de/10010800986
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...
Persistent link: https://www.econbiz.de/10010937355
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...
Persistent link: https://www.econbiz.de/10010938093
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...
Persistent link: https://www.econbiz.de/10010938961
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap...
Persistent link: https://www.econbiz.de/10009469074
Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate...
Persistent link: https://www.econbiz.de/10009469241
Persistent link: https://www.econbiz.de/10005428677
Persistent link: https://www.econbiz.de/10005429396
Persistent link: https://www.econbiz.de/10005429526