To combine or not to combine? issues of combining forecasts

Journal of Forecasting

Volumn 11, Issue 8, 1992, Pages 687-701

  Palm, Franz C ^a   Zellner, Arnold ^b  

^a MAASTRICHT UNIVERSITY   (Netherlands)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

Bayesian methods; Combining forecasts; Model uncertainty

Indexed keywords

EID: 84979340104     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.3980110806     Document Type: Article

References (30)

1. Bayesian consensus forecasts of macroeconomic variables
2. Linear combinations of forecasts: a general Bayesian model
3. Combining forecasts: the end of the beginning or the beginning of the end
4. An empirical study of budget‐related predictions of corporate executives
5. Aggregating subjective forecasts: some empirical evidence
6. The combination of forecasts
7. The portfolio choice problem: comparison of certainty equivalence and optimal Bayes portfolios
8. A comparative evaluation of the outperformance and minimum variance procedures for the linear synthesis of forecasts
9. Combining forecasts: a review and annotated bibliography
10. Forecast combination and encompassing, reconciling two divergent literatures
11. Structural change and the combination of forecasts
12. The use of prior information in forecast combination
13. Unit weighting schemes for decision making
14. Bayesian comparison of simple macroeconomic models
15. Invited review: combining forecaststwenty years later
16. Improved methods of forecasting
17. Inference about variance components in the one way model
18. International portfolio diversification with estimation risk
19. Reconciliation of probability distributions
20. Experience with forecasting univariate time series and the combination of forecasts' (with discussion)
21. Analysts’ forecasts as earnings expectations
22. Combining probability distributions from dependent information sources
23. Combining forecasts: a philosophical basis and some current issues
24. The combination of forecasts
25. Biased predictors, rationality and the evaluation of forecasts