Heteroskedasticity and Neglected Parameter Heterogeneity
通过蒙特卡洛实验说明，在线性回归中忽视参数异质性会导致系数无经济意义，并产生显著的异方差性检验统计量，因此异方差性不应直接使用White稳健标准误估计量。
The paper studies the consequences of neglecting parameter heterogeneity for the linear regression model and cross‐sectional data. Monte‐Carlo experiments are used to illustrate that neglected parameter heterogeneity typically leads to (a) regression coefficients that are economically meaningless and (b)significant test statistics for heteroskedasticity and, possibly non‐normality. The paper concludes that evidence for heteroskedasticity should not routinely lead to the use of White's well‐known heteroskedasticity‐consistent variance covariance matrix estimator. If heteroskedasticity is caused by neglected parameter heterogeneity or other causes of heteroskedasticity, such as wrong functional form, White's estimator will not serve any useful purpose.