Computational Statistics & Data Analysis
Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap
Computational Statistics & Data Analysis
White noise assumptions revisited: regression metamodels and experimental designs in practice
Proceedings of the 38th conference on Winter simulation
Half-life estimation based on the bias-corrected bootstrap: A highest density region approach
Computational Statistics & Data Analysis
Editorial: Nonparametric and Robust Methods
Computational Statistics & Data Analysis
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Evidence is presented on the finite sample performance of tests that are robust to heteroskedasticity. In contrast to previous work, the focus is on testing several restrictions on the coefficients of a linear regression model, rather than on a quasi-t test of a single restriction. Tests based upon different forms of a heteroskedasticity-consistent covariance matrix estimator are examined, as are the relative merits of asymptotic and wild bootstrap critical values. As an alternative to such tests, procedures using the classical F statistic are investigated. These procedures use single and double wild bootstraps to assess the significance of the F statistic. The costs of using heteroskedasticity-robust tests when the errors are actually homoskedastic are discussed.