Article ID Journal Published Year Pages File Type
416221 Computational Statistics & Data Analysis 2006 19 Pages PDF
Abstract

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-tt 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 FF statistic are investigated. These procedures use single and double wild bootstraps to assess the significance of the FF statistic. The costs of using heteroskedasticity-robust tests when the errors are actually homoskedastic are discussed.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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