@article{Forecasting:1636,
      recid = {1636},
      author = {Samuels, Jon D. and Sekkel, Rodrigo},
      title = {Forecasting with Many Models: Model Confidence Sets and  Forecast Combination},
      address = {2013},
      pages = {1 online resource (iii, 47 pages)},
      abstract = {A 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.},
      url = {http://www.oar-rao.bank-banque-canada.ca/record/1636},
      doi = {https://doi.org/10.34989/swp-2013-11},
}