Consistency of the OLS Bootstrap for Independently but Not-Identically Distributed Data: A Permutation Perspective

This paper introduces a new approach to proving bootstrap consistency based upon the distribution of permutation statistics, using it to derive results covering fundamentally not-identically distributed groups of data, in which average moments do not converge to anything, with moment conditions that are less demanding than earlier results for either identically distributed or not-identically distributed data.

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Publication Details

Journal
Econometrics
Published
2025-10-23
DOI
https://doi.org/10.3390/econometrics13040041
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Consistency of the OLS Bootstrap for Independently but Not-Identically Distributed Data: A Permutation Perspective

Alwyn Young
Econometrics
Data Mining Algorithms and Applications
article

Consistency of the OLS Bootstrap for Independently but Not-Identically Distributed Data: A Permutation Perspective

Alwyn Young
article en

Abstract

This paper introduces a new approach to proving bootstrap consistency based upon the distribution of permutation statistics, using it to derive results covering fundamentally not-identically distributed groups of data, in which average moments do not converge to anything, with moment conditions that are less demanding than earlier results for either identically distributed or not-identically distributed data.

EconometricsVol. 13(4)
London School of Economics and Political Science (GB)
Openalex Percentile: Top 56%
Data Mining Algorithms and Applications
DOIOpen Access PDFOpenAlex
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Consistency of the OLS Bootstrap for Independently but Not-Identically Distributed Data: A Permutation Perspective — Alwyn Young · Econometrics (2025) | TGRS Research Map | TGRS