Amazingly Versatile Durbin Regressions with Persistent and Nonlinear Errors: HAC Comparisons

Durbin regressions are found to be remarkably successful in terms of estimating static regression coefficients in the presence of regression errors which may contain long memory or non linear components. The paper extends Baillie, Diebold, Kapetanios, Kim and Mora (2025) which focuses on weakly stationary AR errors to this wider context. The results suggest that Durbin regressions should the preferred approach for estimation of time series regressions. The paper also documents the poor performance of OLS-HAC inference when applied to these regressions.