Autocorrelation in residuals can be addressed using several methods. One common approach is to use a transformation of the data, such as differencing or logging. This can help stabilize variance and reduce autocorrelation. Another method is to incorporate an autoregressive term into your model if it’s appropriate for your data.
In some cases, you may need to reconsider your model entirely. Autocorrelation often indicates that there’s information in the data that isn’t being captured by the current model. You might need to include additional variables or consider a different type of model altogether.
If these strategies don’t work, you could also try using robust standard errors, which are less sensitive to violations of assumptions like no autocorrelation. However, this should be a last resort, as it doesn’t address the underlying issue causing the autocorrelation.
