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When the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI)....
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these models. We implement a Bayesian shrinkage combination methodology to include information that is not captured by the …, these results outperform shrinkage forecasts that consider other priors as equal or zero weights. …
Persistent link: https://www.econbiz.de/10010763698
This paper proposes a new method for combining forecasts based on complete subset regressions. For a given set of potential predictor variables we combine forecasts from all possible linear regression models that keep the number of predictors fixed. We explore how the choice of model complexity,...
Persistent link: https://www.econbiz.de/10010709435
these models. We implement a Bayesian shrinkage combination methodology to include information that is not captured by the …, these results outperform shrinkage forecasts that consider other priors as equal or zero weights. …
Persistent link: https://www.econbiz.de/10010548325
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