- Consulting Editors
- Vars, Cointegration, and Common Cycle Restrictions
- Dynamic Factor Models
- Forecasting With Nonlinear Time Series Models
- Forecasting With DSGE Models
- Forecasting Economic Time Series Using Unobserved Components Time Series Models
- Improving the Role of Judgment in Economic Forecasting
- Forecasting with Mixed-Frequency Data
- Forecasting with Real-Time Data Vintages
- Forecasting from misspecified Models in the Presence of Unanticipated Location Shifts
- Forecasting Breaks and Forecasting During Breaks
- Forecast Combinations
- Multiple Forecast Model Evaluation
- Testing for Unconditional Predictive Ability
- Testing Conditional Predictive Ability
- Interpreting and Combining Heterogeneous Survey Forecasts
- Analyzing Three-Dimensional Panel Data of Forecasts
- Forecasting Financial Time Series
- Forecasting Volatility Using High-Frequency Data
- Economic Value of Weather and Climate Forecasts
- Long-Horizon Growth Forecasting and Demography
- Forecasting the Energy Markets
- Models for Health Care
- Election Forecasting
- Marketing and Sales
Abstract and Keywords
When researchers develop forecasting models, they usually pull down data on a variety of economic variables from a current database. But the potential drawback is that the researcher is using data which are not the same as the data that will face a forecaster in real time. This article discusses issues which arise once it is acknowledged that the values of economic variables taken from the latest available database will not be the same as the data which the forecaster or researcher would have faced in real time. It discusses data revisions and their impact on forecasts, and optimal forecasting when data revisions exist.
Dean Croushore is a professor of economics and Rigsby Fellow at the University of Richmond and serves as the chairman of the Economics Department. The focus of his research in recent years has been on forecasting and how data revisions affect monetary policy, forecasting, and macroeconomic research.
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