Eric Hillebrand

Bagging constrained equity premium predictors

Publikation: Bidrag til bog/antologi/rapport/proceedingBidrag til bog/antologiForskningpeer review

  • Eric Hillebrand
  • Tae-Hwy Lee, University of California, Riverside, USA
  • Marcelo Medeiros, Pontifical Catholic University of Rio de Janeiro, Brasilien
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 and Thompson (2008), we show that we can improve the forecast performance further by smoothing the restriction through bagging.
OriginalsprogEngelsk
TitelEssays in nonlinear time series econometrics
RedaktørerNiels Haldrup, Mikka Meitz, Pentti Saikkonen
Antal sider28
UdgivelsesstedCambridge
ForlagOxford University Press
Udgivelsesår2014
ISBN (trykt)978-0-19-967995-9
DOI
StatusUdgivet - 2014

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