Fast cluster bootstrap methods for linear regression models

James MacKinnon

No 1465, Working Paper from Economics Department, Queen's University

Abstract: Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For OLS regression, a new algorithm is provided for the pairs cluster bootstrap, and two algorithms for the wild cluster bootstrap are compared. One of these is a new way to express an existing algorithm, and the other is new. For IV regression, an algorithm is provided for the wild restricted efficient cluster (WREC) bootstrap, which up to now has been computationally burdensome for large samples. These algorithms are remarkably fast because all computations are based on matrices and vectors that contain sums over the observations within each cluster, which have to be computed just once before the bootstrap loop begins. Monte Carlo experiments are used to study the finite\tkk-sample properties of bootstrap Wald tests for OLS regression and of WREC bootstrap tests for IV regression.

Keywords: clustered data; cluster-robust variance estimator; CRVE; robust inference; wild cluster bootstrap; WCR bootstrap; pairs cluster bootstrap; wild restricted efficient cluster bootstrap; WREC bootstrap; bootstrap Wald test (search for similar items in EconPapers)
JEL-codes: C12 C15 C21 C23 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2021-09
New Economics Papers: this item is included in nep-ecm, nep-isf and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Fast cluster bootstrap methods for linear regression models (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1465

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