The homoskedasticity assumption in time series regression suggests that the variance of the error term cannot be a function of time.

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Homoskedasticity assumes constant variance of errors over time, which implies that the error term is not a function of time. If the variance of the error term changes with time, then it violates the homoskedasticity assumption, leading to biased and inefficient estimators.

The Homoskedasticity Assumption in Time Series Regression Suggests That the Variance