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Understanding Heteroskedasticity and Autocorrelation in Regression Analysis

This article explores the concepts of heteroskedasticity and autocorrelation, two significant issues in regression analysis that violate the assumption E(ee') = s²I. Specifically, we examine how autocorrelation contravenes E(et, et-1) = 0. While autocorrelation keeps the coefficients unbiased, it adversely affects standard errors and t-tests, often making standard errors too small. Recognizing and addressing these issues is crucial for accurate statistical inference in regression models.

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Understanding Heteroskedasticity and Autocorrelation in Regression Analysis

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