Съдържанието не е налично на български език.
Federico Masera
- 23 May 2014
- WORKING PAPER SERIES - No. 1679Details
- Abstract
- In this paper, we exploit micro data from the ECB Survey of Professional Forecasters (SPF) to examine the link between the characteristics of macroeconomic density forecasts (such as their location, spread, skewness and tail risk) and density forecast performance. Controlling for the effects of common macroeconomic shocks, we apply cross-sectional and fixed effect panel regressions linking such density characteristics and density forecast performance. Our empirical results suggest that many macroeconomic experts could systematically improve their density performance by correcting a downward bias in their variances. Aside from this shortcoming in second moment characteristics of the individual densities, other higher moment features, such as skewness or variation in the degree of probability mass given to the tails of the predictive distributions tend - as a rule - not to contribute significantly to enhancing individual density forecast performance.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
- 26 April 2013
- WORKING PAPER SERIES - No. 1540Details
- Abstract
- We propose methods to evaluate the risk assessments collected as part of the ECB Survey of Professional Forecasters (SPF). Our approach focuses on direction-of-change predictions as well as the prediction of relatively more extreme macroeconomic outcomes located in the upper and lower regions of the predictive densities. For inflation and GDP growth, we find such surveyed densities are informative about future direction of change. Regarding more extreme high and low outcome events, the surveys are really only informative about GDP growth outcomes and at short-horizons. The upper and lower regions of the predictive densities for inflation are much less informative.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
- 11 July 2012
- WORKING PAPER SERIES - No. 1446Details
- Abstract
- In this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the evaluation of the entire predictive densities, including an evaluation of the impact of density features such as location, spread, skew and tail risk on density forecast performance. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. Relative to a set of simple benchmarks, this performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of some improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Moreover, higher moment features of expert densities, such as skew or the degree of probability mass in their tails, are shown not to contribute significantly to improvements in individual density forecast performance.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods