Fast cluster bootstrap methods for linear regression models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Queen’s Economics Department Working Paper ; No. 1465

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Statistical Simulation Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Subject: 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

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): MacKinnon, James G.
• Event: Veröffentlichung
• (who): Queen's University, Department of Economics
• (where): Kingston (Ontario)
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/247206
• Last update: 10.03.2025, 11:45 AM CET