A sequence of improved standard errors under heteroskedasticity of unknown form

作者：Cribari Neto Francisco^*; Lima Maria da Gloria A

来源：Journal of Statistical Planning and Inference, 2011, 141(11): 3617-3627.

DOI：10.1016/j.jspi.2011.05.015

• Bias
• Covariance matrix estimation
• Heteroskedasticity
• Linear regression

The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and a set of explanatory variables. The assumption that all error variances are the same (homoskedasticity) is oftentimes violated. Consistent regression standard errors can be computed using the heteroskedasticity-consistent covariance matrix estimator proposed by White (1980). Such standard errors, however, typically display nonnegligible systematic errors in finite samples, especially under leveraged data. Cribari-Neto et al. (2000) improved upon the White estimator by defining a sequence of bias-adjusted estimators with increasing accuracy. In this paper, we improve upon their main result by defining an alternative sequence of adjusted estimators whose biases vanish at a much faster rate. Hypothesis testing inference is also addressed. An empirical illustration is presented.

• 出版日期2011-11