Autocorrelated errors are not usually a concern for regression models using cross-sectional data.

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Autocorrelation is a phenomenon where the error terms in a regression model are correlated with each other, indicating that there is some systematic pattern in the residuals. This is usually a concern for time series data, where observations are taken at multiple time points and there may be some temporal dependencies between them. In cross-sectional data, however, each observation is independent of the others and there is no natural ordering or timing to the data. As a result, autocorrelation is not usually an issue in cross-sectional regression models.

Autocorrelated Errors Are Not Usually a Concern for Regression Models