A Comparison of Heteroscedasticity Robust Standard Errors and Nonparametric Generalized Least Squares
نویسندگان
چکیده
This paper presents a Monte Carlo comparison of several versions of heteroscedasticity robust standard errors (HRSEs) to a nonparametric feasible generalized least squares procedure (NPGLS). Results suggest that the NPGLS procedure provides an improvement in efficiency ranging from 3% to 12% or more in reasonable sample sizes using simple functional forms for heteroscedasticity. This results in tighter confidence intervals and more precise estimation and inference. Thus, the NPGLS estimator provides nearly identically sized hypothesis tests, with a significant gain in power. JEL Classification: C13 (Estimation), C14 (Semiparametric and nonparametric methods)
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