Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/176087 
Year of Publication: 
2012
Series/Report no.: 
Texto para discussão No. 604
Publisher: 
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, Rio de Janeiro
Abstract: 
The literature on excess return prediction has considered a wide array of estimation schemes, among them unrestricted and restricted regression coefficients. We consider bootstrap aggregation (bagging) to smooth parameter restrictions. Two types of restrictions are considered: positivity of the regression coefficient and positivity of the forecast. Bagging constrained estimators can have smaller asymptotic mean-squared prediction errors than forecasts from a restricted model without bagging. Monte Carlo simulations show that forecast gains can be achieved in realistic sample sizes for the stock return problem. In an empirical application using the data set of Campbell, J., and S. Thompson (2008): “Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?”, Review of Financial tudies 21, 1511-1531, we show that we can improve the forecast performance further by smoothing the restriction through bagging.
JEL: 
C5
E4
G1
Document Type: 
Working Paper

Files in This Item:
File
Size
253.33 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.