Bruce E. Hansen and Jeffrey Racine
"Jackknife Model Averaging"
July 2007
Abstract:
We consider the problem of obtaining appropriate weights for
averaging approximate (misspecified) models for improved
estimation of an unknown conditional mean in the face of model
uncertainty in heteroskedastic error settings. We propose a
"jackknife model averaging" (JMA) estimator which selects the
weights by minimizing a cross-validation criterion. In models that
are linear in the parameters, this criterion is quadratic in the
weights, so computation of the weights is a simple application of
quadratic programming. We show that our estimator is
asymptotically optimal in the sense of achieving the lowest
possible expected squared error. Monte Carlo simulations and an
illustrative application show that JMA can achieve significant
efficiency gains over existing model selection and averaging
methods in the presence of heteroskedasticity.
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Some of the above material is based upon work supported by the National Science Foundation under Grants No. SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111.
Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s), and do not necessarily reflect the views of the NSF.