Jackknife instrumental variable estimation with heteroskedasticity

Paulus Bekker, Federico Crudu

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)


We present a new jackknife estimator for instrumental variable inference with unknown heteroskedasticity.
It weighs observations such that many-instruments consistency is guaranteed while the signal
component in the data is maintained. We show that this results in a smaller signal component in the
many instruments asymptotic variance when compared to estimators that neglect a part of the signal to
achieve consistency. Both many strong instruments and many weak instruments asymptotic distributions
are derived using high-level assumptions that allow for instruments with identifying power that varies
between explanatory variables. Standard errors are formulated compactly. We review briefly known estimators
and show in particular that our symmetric jackknife estimator performs well when compared
to the HLIM and HFUL estimators of Hausman et al. in Monte Carlo experiments.
Original languageEnglish
Pages (from-to)332–342
Number of pages11
JournalJournal of Econometrics
Issue number2
Early online date23-Dec-2014
Publication statusPublished - 2015

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