Asymptotic inference under heteroskedasticity of unknown form

By Francisco Cribari‐Neto. Computational Statistics & Data Analysis, 2004

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/38ddb7f84e1e6fe00e661177f9584fcdabef66d1

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In Statistics and Probability

Citations in the corpus

Bootstrap Tests for Regression Models (2009, methodology): “This strategy for obtaining regressor values is often adopted in simulation studies, see, for example, Cribari-Neto and Zarkos (1999) for references.”
Resurrecting weighted least squares (2017, unknown): “On the other hand, we are only aware of a single simulation study evaluating the performance of the HC4 estimator outside of Cribari-Neto (2004)10:MacKinnon (2012) advises against the use of the HC4 estimator, since corresponding inference can underreject severely and can lack power.”
Panel data econometrics in R: The plm package (2008, methodology): “The errors may be weighted according to the schemes proposed by MacKinnon and White (1985) and Cribari-Neto (2004) to improve small-sample performance 18 .”
The positive and negative semantic dimensions of relationship satisfaction (2013, methodology): “To correct for this, we employed the heteroskedasticityconsistent standard error estimate procedure developed by Cribari-Neto (2004), and computed the analyses using the SPSS macro and syntax provided by Hayes and Cai (2007). Similarly, Levene’s test indicated significant violation of the homogeneit”
Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation (2007, methodology): “, Cai & Hayes, in press), and other promising methods, such as bootstrapping—sometimes combined with HCSE estimators (Cribari-Neto, 2004; Godfrey, 2006; Godfrey & Orne, 2004; Wilcox, 2005)—have been investigated.”
Heteroskedasticity-Robust Inference in Finite Samples (2012, unknown): “HC4 (Cribari-Neto, 2004) adjusts the residuals by a leverage factor that increases with the leverage.”
Object-oriented Computation of Sandwich Estimators (2006, background): “HC estimators In addition to the two HC estimators that can be written as outer product estimators (also called HC0 and HC1), various other HC estimators (usually called HC2–HC4) have been suggested, in particular for the linear regression model (MacKinnon and White 1985; Long and Ervin 2000; Cribar”
Neglected biological patterns in the residuals (2011, methodology): “However, the HC4 method performs better when there are certain points with very high LEVERAGEs (Cribari-Neto 2004).”
Thirty Years of Heteroskedasticity-Robust Inference (2013, background): “Cribari-Neto and Lima (2009) provide simulation results which suggest that, for the set of models they study, the coverage of confidence intervals based on HCj for j = 0, .”
Econometric Computing with HC and HAC Covariance Matrix Estimators (2004, unknown): “In his analysis, Cribari-Neto (2004) uses his HC4 estimator (among others) giving the following result:”