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Bootstrap Standard Error Estimates and Inference
Author(s) -
Hahn Jinyong,
Liao Zhipeng
Publication year - 2021
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta17912
Subject(s) - estimator , consistency (knowledge bases) , inference , mathematics , variance (accounting) , weak convergence , delta method , asymptotic distribution , econometrics , convergence (economics) , asymptotic analysis , central limit theorem , standard error , limit (mathematics) , distribution (mathematics) , moment (physics) , statistics , economics , computer science , mathematical analysis , physics , discrete mathematics , accounting , computer security , artificial intelligence , asset (computer security) , economic growth , classical mechanics
Asymptotic justification of the bootstrap often takes the form of weak convergence of the bootstrap distribution to some limit distribution. Theoretical literature recognized that the weak convergence does not imply consistency of the bootstrap second moment or the bootstrap variance as an estimator of the asymptotic variance, but such concern is not always reflected in the applied practice. We bridge the gap between the theory and practice by showing that such common bootstrap based standard error in fact leads to a potentially conservative inference.