Heteroscedasticity §

Group: 4 #group-4

Relations §

• Linear Regression: Heteroscedasticity refers to the violation of the assumption of constant variance of the error terms in a linear regression model.
• Robust Standard Errors: Robust standard errors, such as Huber-White standard errors, can be used to obtain valid inference in the presence of heteroscedasticity.
• Weighted Least Squares: Weighted Least Squares (WLS) is a method used to address heteroscedasticity by weighting observations based on their variance.
• Violation of Assumptions: Heteroscedasticity is a violation of the assumption of constant variance of errors in regression models.
• Heteroskedasticity-Consistent Standard Errors: Heteroskedasticity-Consistent Standard Errors (HCSE) are a type of robust standard errors that provide valid inference in the presence of heteroscedasticity.
• Linear Regression: Heteroscedasticity is a common issue in linear regression models, where the assumption of constant variance of errors is often violated.
• Generalized Least Squares: Generalized Least Squares (GLS) is a method that can be used to address heteroscedasticity by incorporating the variance structure of the error term.
• Heterogeneity: Heteroscedasticity is a specific case of heterogeneity of variance in regression analysis, where the variability of errors is unequal across different values of the independent variable.
• Variance: Heteroscedasticity refers to the situation where the variance of the error term is not constant across observations.
• Homoscedasticity: Heteroscedasticity is the violation of the homoscedasticity assumption, which states that the variance of the error term is constant across all levels of the independent variables.
• Breusch-Pagan Test: The Breusch-Pagan test is a statistical test used to detect heteroscedasticity in a regression model.
• Biased Standard Errors: Heteroscedasticity can lead to biased standard errors in Ordinary Least Squares (OLS) models, which can result in invalid statistical inference.
• Inefficient Estimates: In the presence of heteroscedasticity, the Ordinary Least Squares (OLS) estimators are still unbiased but no longer efficient.
• White Test: The White test is another statistical test used to detect heteroscedasticity in a regression model.
• Ordinary Least Squares: Ordinary Least Squares (OLS) estimation assumes homoscedasticity, and heteroscedasticity can lead to inefficient and biased standard errors in OLS models.
• Residual Analysis: Residual analysis, such as plotting residuals against predicted values or independent variables, can be used to visually inspect for heteroscedasticity.