- 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 studies two issues in forecast combination, first considering ways to combine forecasts from surveys and time series models. Second, it considers the possibility, advanced by Hendry and Clements (2004), that model instability can help explain the gains in forecasting performance resulting from combination. The article is organized as follows. Section 2 discusses the design of the universe of forecasting models used in combining forecasts from time series models and subjective survey forecasts. Section 3 undertakes an empirical analysis using forecasts from univariate and multivariate linear models, nonlinear models, and survey forecasts. Section 4 provides analytical results that shed light on the performance of forecast combinations under model instability. Section 5 presents empirical results on forecast combinations under breaks. Section 6 concludes.
Marco Aiolfi, Phd, joined GSAM's Quantitative Investment Strategies Portfolio Management/Research team in January 2010. Prior to this, he was a principal at Platinum Grove Asset Management with research and trading responsibilities for currencies strategies. Previously he was a research scholar at the University of California, San Diego specializing in macro asset pricing and econometrics. In 2005 he was a visiting scholar for the Research Department of the International Monetary Fund. Marco has contributed articles to several academic journals including the Journal of Development Economics, the Journal of Financial Econometrics, the Journal of Econometrics, and the Journal of Forecasting. He received his PhD in economics from Bocconi University in 2006.
Carlos Capistrán is senior research economist in the General Directorate of Economic Research at Banco de México. He has taught at universities in Mexico and the United States and has given seminars at universities and central banks around the world on economic forecasting, monetary policy, and related topics. Carlos has published various articles in international refereed journals such as the Journal of Monetary Economics and the Journal of Business and Economics Statistics. He received his PhD in economics from the University of California, San Diego in 2005.
Allan Timmermann holds the Atkinson/Epstein Chair in Management Leadership at the Rady School at the University of California, San Diego, where he is also a professor of management and economics. He obtained his PhD from University of Cambridge and has written numerous papers on predicability of stock market returns, technical trading rules, and performace evaluation for mutual funds and pension funds. This work has been published in a range of journals, including the Journal of American Statistcal Association, Journal of Finance, Journal of Business, Nature, Journal of Econometrics, Journal of Forecasting, and Quarterly Journal of Economics. His paper on sign predictability, coauthored with Hashem Pesaran, was awarded the "best paper award" for a paper published in the International Journal of Forecasting over the two-year period 2004-2005.
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