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Choices between OLS with robust inference and feasible GLS in time series regressions

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  • Baillie, Richard T.
  • Kim, Kun Ho

Abstract

We consider the practice of estimating static regressions by OLS from time series data and using robust standard errors for inference. Depending on the form of exogeneity being violated, the asymptotic bias of OLS can exceed that of GLS. Feasible GLS, where the error process is approximated by a sieve autoregression, can dominate the OLS approach with robust standard errors both in terms of bias and MSE for some regions of the parameter space.

Suggested Citation

  • Baillie, Richard T. & Kim, Kun Ho, 2018. "Choices between OLS with robust inference and feasible GLS in time series regressions," Economics Letters, Elsevier, vol. 171(C), pages 218-221.
    • Handle: RePEc:eee:ecolet:v:171:y:2018:i:c:p:218-221
    DOI: 10.1016/j.econlet.2018.07.036
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    References listed on IDEAS

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    More about this item

    Keywords

    OLS; GLS; Feasible GLS; Asymptotic bias; Robust inference;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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