Tactical Industry Allocation and Model Uncertainty
AbstractWe use Bayesian model averaging to analyze industry return predictability in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out-of-sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out-of-sample forecasting power of a naive i.i.d. forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk. Copyright (c)2008, The Eastern Finance Association.
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Bibliographic InfoArticle provided by Eastern Finance Association in its journal Financial Review.
Volume (Year): 43 (2008)
Issue (Month): 2 (05)
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- Maltritz, Dominik & Molchanov, Alexander, 2013. "Analyzing determinants of bond yield spreads with Bayesian Model Averaging," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5275-5284.
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