A panel of ex-ante forecasts of a single time series is modeled as a dynamic factor model, where the conditional expectation is the single unobserved factor. When applied to out-of-sample forecasting, this leads to combination forecasts that are based on methods other than OLS. These methods perform well in a Monte Carlo experiment. These methods are evaluated empirically in a panel of simulated real-time computer-generated univariate forecasts of U.S. macroeconomic time series.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Rights and permissions
About this article
Cite this article
Chan, Y., Stock, J. & Watson, M. A dynamic factor model framework for forecast combination. Span Econ Rev 1, 91–121 (1999). https://doi.org/10.1007/s101080050005
- JEL classification: C32, C22
- Key words: Combination forecasts, principal component regression, James-Stein estimation