Quantitative Economics
Journal Of The Econometric Society
Edited by: Stéphane Bonhomme • Print ISSN: 1759-7323 • Online ISSN: 1759-7331
Edited by: Stéphane Bonhomme • Print ISSN: 1759-7323 • Online ISSN: 1759-7331
Quantitative Economics: Jul, 2024, Volume 15, Issue 3
https://doi.org/10.3982/QE2350
p. 783-816
Jinyong Hahn, David W. Hughes, Guido Kuersteiner, Whitney K. Newey
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 higher‐order 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 higher‐order variance term that is twice as large as that of the “leave‐one‐out” jackknife.
Jinyong Hahn, David W. Hughes, Guido Kuersteiner, and Whitney K. Newey
The replication package for this paper is available at https://doi.org/10.5281/zenodo.10957107. The Journal checked the data and codes included in the package for their ability to reproduce the results in the paper and approved online appendices.