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.
Heteroscedasticity in Regression Heteroscedasticity in Regression: 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 leads to biased and inefficient estimators of the regression coefficients. …
Data is taken from the Economic Survey of Pakistan 1991-1992. The data file link is at the end of the post “Goldfeld-Quandt Test Example for the Detection of Heteroscedasticity”. Read about the Goldfeld-Quandt Test in detail by clicking the link “Goldfeld-Quandt Test: Comparison of Variances of Error Terms“. Goldfeld-Quandt Test …
The post is about Heteroscedasticity Residual Plot. Heteroscedasticity and Heteroscedasticity Residual Plot One of the assumptions of the classical linear regression model is that there is no heteroscedasticity (error terms have constant error terms) meaning that ordinary least square (OLS) estimators are (BLUE, best linear unbiased estimator) and their variances …
Heteroscedasticity refers to a situation in which the variability of the errors (residuals) in a regression model is not constant across all levels of the independent variable(s). It refers to the violation of the assumption of homoscedasticity in linear regression models (LRM). Heteroscedasticity Consequences A short detail about the Heteroscedasticity …