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A multi-step kernel–based regression estimator that adapts to error distributions of unknown form

Author

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  • De Gooijer, Jan G.
  • Reichardt, Hugo

Abstract

For linear regression models, we propose and study a multi-step kernel density-based estimator that is adaptive to unknown error distributions. We establish asymptotic normality and almost sure convergence. An efficient EM algorithm is provided to implement the proposed estimator. We also compare its finite sample performance with five other adaptive estimators in an extensive Monte Carlo study of eight error distributions. Our method generally attains high mean-square-error efficiency. An empirical example illustrates the gain in efficiency of the new adaptive method when making statistical inference about the slope parameters in three linear regressions.

Suggested Citation

  • De Gooijer, Jan G. & Reichardt, Hugo, 2021. "A multi-step kernel–based regression estimator that adapts to error distributions of unknown form," LSE Research Online Documents on Economics 115083, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:115083
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    File URL: http://eprints.lse.ac.uk/115083/
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    References listed on IDEAS

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    More about this item

    Keywords

    adaptive estimation; EM algorithm; kernel density estimate; least squares estimate; linear regression;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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