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Activity Number:
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344
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #302754 |
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Title:
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Bagging Constrained Forecasts with Application to Forecasting Equity Premium
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Author(s):
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Eric Hillebrand and Tae-Hwy Lee*+ and Marcelo C. Medeiros
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Companies:
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Louisiana State University and University of California, Riverside and Pontifical Catholic University Rio
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Address:
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Department of Economics, Riverside, CA, 92521-0427,
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Keywords:
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bagging ; equity premium ; restricted estimation ; restricted forecast ; out-of-sample forecast
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Abstract:
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The literature on excess return prediction has considered a wide array of estimation schemes, among them unrestricted and restricted regression coefficients. We propose bootstrap aggregation (bagging) as a means of imposing parameter restrictions. In this context, bagging results in a soft threshold as opposed to the hard threshold that is implied by a simple restricted estimation. We show analytically that the resulting forecast has lower variance than the forecast that results from a simple restricted estimator. In an empirical application using the same data set as in Campbell and Thompson (2008), "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?", we show that the resulting forecasts have more predictive power than those resulting from simple parameter restrictions.
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