Efficient bias correction for cross‐section and panel data

Jinyong Hahn, David W. Hughes, Guido Kuersteiner and Whitney K. Newey

Quantitative Economics, 2024, vol. 15, issue 3, 783-816

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 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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.3982/QE2350

Related works:
Working Paper: Efficient Bias Correction for Cross-section and Panel Data (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:15:y:2024:i:3:p:783-816

Ordering information: This journal article can be ordered from
https://www.econometricsociety.org/membership

Access Statistics for this article

More articles in Quantitative Economics from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().