Statistics MCQs, Analysis, Software
Lecture notes about Heteroscedasticity, its consequences, detection, and remedy. Hetero or Heteroscedasticity is a violation of the OLS assumption of consistent variance.
OLS Estimation Method is a widely used method in regression analysis for the estimation of the parameters used in a linear regression model. However, when heteroscedasticity exists (which refers to the situation where the variance of the error terms is not constant across observations) the assumptions of OLS may be …
Let us start with the nature of heteroscedasticity. The assumption of homoscedasticity (equal spread, equal variance) is $$E(u_i^2)=E(u_i^2|X_{2i},X_{3i},\cdots, X_{ki})=\sigma^2,\quad 1,2,\cdots, n$$ The above Figure shows that the conditional variance of $Y_i$ (which is equal to that of $u_i$), conditional upon the given $X_i$, remains the same regardless of the values …
The pose is about a general discussion and an introduction to heteroscedasticity. Introduction Heteroscedasticity and Homoscedasticity The term heteroscedasticity refers to the violation of the assumption of homoscedasticity in linear regression models (LRM). In the case of heteroscedasticity, the errors have unequal variances for different levels of the regressors, which …
The post is about Remedial Measures of Heteroscedasticity. Heteroscedasticity is a condition in which the variance of the residual term, or error term, in a regression model, varies widely. The heteroscedasticity does not destroy the unbiasedness and consistency properties of the OLS estimator (as OLS estimators remain unbiased and consistent …