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Amazingly versatile Durbin regressions with persistent and nonlinear errors: HAC comparisons

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  • Baillie, Richard T.
  • Kapetanios, George
  • Kim, Kun Ho

Abstract

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.

Suggested Citation

  • Baillie, Richard T. & Kapetanios, George & Kim, Kun Ho, 2025. "Amazingly versatile Durbin regressions with persistent and nonlinear errors: HAC comparisons," Economics Letters, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:ecolet:v:257:y:2025:i:c:s0165176525005336
    DOI: 10.1016/j.econlet.2025.112696
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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