Simulation study on bootstrap confidence intervals in linear models: Case of heteroscedasticity
Department of Economics and Business, Westminster International University in Tashkent, Tashkent city, Uzbekistan.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 2250–2259
Publication history:
Received on 08 August 2024; revised on 14 September 2024; accepted on 17 September 2024
Abstract:
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.
Keywords:
Heteroscedasticity; Homoscedasticity; Linear model; Confidence Interval; Bootstrap; Accuracy'
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