Forecasting with Many Models: Model Confidence Sets and Forecast Combination
AbstractA longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging. We compare different trimming schemes and propose a new one based on Model Confidence Sets that take into account the statistical significance of historical out-of-sample forecasting performance. In an empirical application of forecasting U.S. macroeconomic indicators, we find significant gains in out-of-sample forecast accuracy from our proposed trimming method.
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Bibliographic InfoPaper provided by Bank of Canada in its series Working Papers with number 13-11.
Length: 50 pages
Date of creation: 2013
Date of revision:
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Econometric and statistical methods;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-05-05 (All new papers)
- NEP-ECM-2013-05-05 (Econometrics)
- NEP-ETS-2013-05-05 (Econometric Time Series)
- NEP-FOR-2013-05-05 (Forecasting)
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