Homoscedasticity - Yousef's Notes
Homoscedasticity

Homoscedasticity

Assumption that the variance of errors or residuals is constant across all levels of an independent variable in a #ml/regression model.

#Importance

  • Ensures efficient and unbiased parameter estimates using OLS.
  • Maintains validity for statistical tests like t-tests and F-tests.
  • Supports accurate construction of confidence intervals.

#Detection

  • Visual inspection via residual plots (expect random scatter without patterns).
  • Statistical tests such as Breusch-Pagan or White’s test.

#Consequences of Violation

  • Leads to inefficient estimates if unaddressed.
  • Results in invalid hypothesis testing and confidence intervals.

#Solutions for Heteroscedasticity:

  • Transformations (e.g., log transformation) to stabilize variance.
  • Use robust standard errors or Weighted Least Squares (WLS).