Robust standard errors are a statistical method used to adjust the standard errors of regression coefficients to account for heteroscedasticity or other violations of standard regression assumptions. This adjustment leads to more reliable hypothesis tests and confidence intervals, especially in econometric models where the data may not meet classical assumptions. This technique is crucial in financial modeling, where real-world data often displays non-constant variability.
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