When should Newey-West standard errors be preferred over standard robust (White) standard errors in Python time series analysis?

Newey-West standard errors should be preferred over standard robust (White) standard errors when there is evidence or strong suspicion of *both* heteroskedasticity *and* autocorrelation in the error terms of your time series regression. White standard errors (e.g., `cov_type='HC0'`, `HC1'`, etc., in `statsmodels`) only correct for heteroskedasticity. Newey-West standard errors, being Heteroskedasticity and Autocorrelation Consistent (HAC), simultaneously address both issues, which is a common characteristic of time series data and crucial for valid inference in such contexts. If only heteroskedasticity is present and errors are independent, White standard errors are sufficient.