Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms
Yongchen Zhao ()
No 2015-04, Working Papers from Towson University, Department of Economics
Based on a set of carefully designed Monte Carlo exercises, this paper documents the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are cross-sectionally heterogeneous and dynamically evolving over time. Results from these exercises provide guidelines regarding the selection of forecast combination method based on the nature, frequency, and magnitude of instabilities in forecasts as well as the target variable. Following these guidelines, a simple forecast combination procedure is proposed and demonstrated through a real-time forecast combination exercise using the U.S. Survey of Professional Forecasters, where combined forecasts are shown to have superior performance that is not only statistically significant but also of practical importance.
Keywords: Forecast combination; Exponential re-weighting; Shrinkage; Estimation error; Performance stability; Real-Time Data. (search for similar items in EconPapers)
JEL-codes: C53 C22 C15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
Date: 2015-12, Revised 2015-12
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Working Paper: Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms (2015)
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