Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/320312 
Year of Publication: 
2024
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 15 [Issue:] 3 [Year:] 2024 [Pages:] 783-816
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higherorder variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher-order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split-sample jackknife, result in larger higher-order variances in the i.i.d. setting we focus on. For both a cross-sectional MLE and a panel model with individual fixed effects, we show that the split-sample jackknife has a higherorder variance term that is twice as large as that of the "leave-one-out" jackknife.
Subjects: 
Bias correction
higher-order variance
bootstrap
jackknife
JEL: 
D13
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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