Some Monte Carlo Evidence on the Relative Efficiency of Parametric and Semiparametric EGLS Estimators
通过蒙特卡洛模拟比较了参数和半参数EGLS估计量在线性回归异方差下的表现，发现小样本中普通最小二乘在低异方差时占优，中等样本中正确设定的EGLS和半参数方法在中高异方差时更优。
One of the most common practical problems in statistics and econometrics is the estimation of linear regression models with heteroscedastic errors. This article reports the results of a Monte Carlo comparison of various parametric and semiparametric estimated generalized least squares (EGLS) estimators. In small-sized (20) and sometimes medium-sized (50) samples, ordinary least squares dominated the other techniques for low levels of heteroscedasticity. In mediumsized samples, correctly specified EGLS dominated with moderate and large levels of heteroscedasticity. Apart from correctly specified EGLS, a semiparametric approach generally dominated in the medium-sized samples with moderate and large amounts of heteroscedasticity. An additional result is that an incorrectly specified EGLS estimator could, in small samples, yield more precise estimates than the other EGLS techniques. For each of the feasible parametric and semiparametric techniques considered, the usual standard errors and heteroscedasticity- consistent standard errors understated the sample variability of the estimators.