Combining forecasts: A review and annotated bibliography

By R. Clemen. 1989

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Layered Ensemble Architecture for Time Series Forecasting (2016, background): “Clemen [13] provides a comprehensive review and annotated bibliography in this area.”
Forecasting in social settings: The state of the art (2020, background): “Combinations have again and again been proved to bring benefits in forecasting accuracy (Makridakis, 1989; Clemen, 1989; Timmermann, 2006) while also decreasing the variance of the forecasts (Hibon and Evgeniou, 2005) thus rendering operational settings more efficient.”
Selection of estimation window in the presence of breaks (2007, background): “For reviews of the forecast combination literature see Clemen (1989), Granger (1989), Diebold and Lopez (1996), and more recently Newbold and Harvey (2002) and Timmermann (2005).”
Combining Forecasts: An Application to Elections (2014, unknown): “As noted previously, Clemen (1989) reviewed the literature on combining forecasts and concluded that equally weighting the individual forecasts is often the best course of action when combining.”
Forecasting Euro Area Inflation: Does Aggregating Forecasts by Hicp Component Improve Forecast Accuracy? (2003, unknown): “Hendry & Clements (2003)).4 Non-linear time series models are not considered in the current paper due to the short time series available for estimation.5 Time-varying parameter models are not considered here, either. Stock & Watson (1996) suggest that gains from using time varying parameter models f”
Simple robust averages of forecasts: Some empirical results (2008, unknown): “In addition, the evidence has shown that a simple average (the mean of the forecasts being combined) often provides better results than more complicated and sophisticated combining models (Clemen, 1989).”
Time series forecasting with neural network ensembles: an application for exchange rate prediction (2001, methodology): “3,17 Although neural network ensemble can be an effective method in reducing model variance and improving neural network forecasting performance, the effectiveness of the ensemble model may be limited if the errors generated by different component models are correlated.”
Is unequal weighting significantly better than equal weighting for multi‐model forecasting? (2013, methodology): “The consistently good performance of the multi-model mean also has been recognized in the economic literature, as documented routinely in the International Journal of Forecasting, Operational Research Quarterly and Journal of Forecasting (Clemen, 1989).”
Judgmental aggregation strategies depend on whether the self is involved (2011, methodology): “Averaging opinions is remarkably robust, and compares favorably to more complex methods of aggregation (Armstrong, 2001; Clemen, 1989; Goodwin & Wright, 1998).”
Short-run price forecast performance of individual and composite models for 496 corn cash markets (2017, background): “Clemen [18] found through a literature review that composite forecasts outperform individual predictions in a wide range of forecasting problems.”
Forecast Combination Across Estimation Windows (2011, unknown): “For each series, we calculate the absolute bias, the RMSFE, and tests for predictive performance of Diebold and Mariano (1995).”
Power, competitiveness, and advice taking: Why the powerful don’t listen (2012, background): “In recent years, there has been increasing interest in the fact that averaging judgments across people increases the accuracy of judgments (Clemen, 1989), which has been termed the ―wisdom of crowds‖ (Surowiecki, 2004).”