Logo image
Using heteroscedasticity-non-consistent or heteroscedasticity-consistent variances in linear regression
期刊文章

Using heteroscedasticity-non-consistent or heteroscedasticity-consistent variances in linear regression

C.Y. (Chor-yiu) SinCheng-Few Lee
Econometrics and Statistics
2020

摘要

Asymptotic properties Finite-sample properties non-robust variance Robust variance Strictly exogenous Weakly dependent Statistics and Probability Economics and Econometrics Statistics Probability and Uncertainty
The properties of the heteroscedasticity non-consistent variances and heteroscedasticity consistent variances are reviewed. Unlike the related existing results, the following cases are discussed separately: (i) the cases where the explanatory variables are strictly exogenous; and (ii) the cases where the explanatory variables may or may not be strictly exogenous. The latter cases allow weakly dependent explanatory variables such as those generating from an autoregressive process. New results on the original robust variance (denoted by HC 0 ) and its variants (denoted by HC 1 , HC 2 , HC 3 , HC 4 and HC j ) are derived. In particular, the followings are shown: (i) the ordering of the original robust variance and its variants; (ii) the asymptotic equivalence among different variants of robust variance; and (iii) under quadratic form of heteroscedasticity (with mesokurtic/leptokurtic normalized error) or GARCH(1,1)-error, non-robust variance rejects more often than robust variance. Simulation studies suggest HC 4 by and large does not over-rejects or mildly under-rejects.

相關連結

指標

1 檢視次數

詳細資料

Logo image