- 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
This article focuses on recent developments in the forecasting literature on how to simultaneously control both the overall error rate and the contribution of irrelevant models. As a novel contribution, it derives a general class of superior predictive ability tests, which controls for family-wise error rate (FWER) and the contribution of irrelevant models. The article is organized as follows. Section 2 defines the setup. Section 3 reviews the approaches that control for the conservative FWER. Section 4 considers a general class of tests characterized by multiple joint inequalities. Section 5 presents results allowing for control of the less conservative false discovery rate. Section 6 considers the model confidence set approach and offers a simple alternative that reduces the influence of irrelevant models in the initial set. Section 7 briefly reviews the empirical evidence, while Section 8 concludes.
Valentina Corradi is currently professor of economics at the University of Warwick. She has previously held positions at the University of Pennsylvania, Queen Mary, University of London and University of Exeter. Her current research interests include forecast stability of factor models, bandwidth selection, predictive densities for volatility and partial identification. She is associate editor of the Review of Economic Studies and the Econometrics Journal. Valentina's recent work has been published in the Review of Economic Studies, International Economic Review, Journal of Econometrics, and Journal of Business Statistics and Economics.
Walter Distaso is professor of financial econometrics at the Imperial College Business School. He has previously held positions at the University of Exeter and Queen Mary, University of London. His research interests include estimation, specification testing, and prediction of financial volatility in continuous time models; analyzing macroeconomic and financial time series using long-memory models; identifying the macroeconomic determinants of stock market volatility; studying the dependence of multivariate financial time series using copulas; analyzing the features and effects of market microstructure noise. His work has been published in the Review of Economic Studies, Journal of Econometrics and Journal of Business and Economic Statistics.
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