A dynamic factor model framework for forecast combination

Yeung Lewis Chan, James H. Stock and Mark Watson
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Yeung Lewis Chan: Department of Economics, Harvard University, Cambridge, MA 02138, USA Kennedy School of Government and NBER, 79 John F. Kennedy Street, Harvard University, Cambridge, MA 02138, USA Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA
James H. Stock: Department of Economics, Harvard University, Cambridge, MA 02138, USA Kennedy School of Government and NBER, 79 John F. Kennedy Street, Harvard University, Cambridge, MA 02138, USA Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA

Spanish Economic Review, 1999, vol. 1, issue 2, 91-121

Abstract: 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.

Keywords: Combination forecasts; principal component regression; James-Stein estimation (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Date: 1999-08-06
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Citations: View citations in EconPapers (47)

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