Simulation study on bootstrap confidence intervals in linear models: Case of heteroscedasticity

OLS models have several assumptions for its interval estimations to be unbiased and efficient. Non-constant variance of residuals can cause serious issues in making inferences on coefficients as well as interval estimations. In this paper, we discuss the presence of heteroscedasticity in a linear model and suggest a paired bootstrap approach as an assumption-free approach on constructing confidence intervals. We carry a simulation study to compare bootstrap confidence intervals to traditional intervals. We conclude bootstrap intervals, though not perfect, can give better interval estimates when heteroscedasticity is observed and no remedy is applied.