Approximate inference in heteroskedastic regressions: A numerical evaluation
The commonly made assumption that all stochastic error terms in the linear regression model share the same variance (homoskedasticity) is oftentimes violated in practical applications, especially when they are based on cross-sectional data. As a precaution, a number of practitioners
choose to base inference on the parameters that index the model on tests whose statistics employ asymptotically correct standard errors, i.e. standard errors that are asymptotically valid whether or not the errors are homoskedastic. In this paper, we use numerical integration methods to evaluate
the finite-sample performance of tests based on different (alternative) heteroskedasticity-consistent standard errors. Emphasis is placed on a few recently proposed heteroskedasticity-consistent covariance matrix estimators. Overall, the results favor the HC4 and HC5 heteroskedasticity-robust
standard errors. We also consider the use of restricted residuals when constructing asymptotically valid standard errors. Our results show that the only test that clearly benefits from such a strategy is the HC0 test.
Keywords: covariance matrix estimation; heteroskedasticity; leverage point; linear regression; quasi-t test
Document Type: Research Article
Affiliations: 1: Departamento de Estatistica, Universidade Federal de Pernambuco, Cidade Universitaria, Recife, PE, Brazil 2: Departamento de Estatistica e Informatica, Universidade Federal Rural de Pernambuco, Recife, PE, Brazil
Publication date: 01 April 2010
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