Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247206 
Year of Publication: 
2021
Series/Report no.: 
Queen’s Economics Department Working Paper No. 1465
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For ordinary least squares (OLS) regression, a new algorithm is provided for the pairs cluster bootstrap, along with two algorithms for the wild cluster bootstrap. One of these is a new way to express an existing method. For instrumental variables (IV) regression, an efficient algorithm is provided for the wild restricted efficient cluster (WREC) bootstrap. All computations are based on matrices and vectors that contain sums of squares and cross-products for 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-sample properties of bootstrap Wald tests for OLS regression and of WREC bootstrap tests for IV regression.
Subjects: 
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
JEL: 
C12
C15
C21
C23
Document Type: 
Working Paper

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