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Bandwidth selection for nonparametric regression with errors-in-variables

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  • Dong, Hao
  • Otsu, Taisuke
  • Taylor, Luke

Abstract

We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross-validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this article lead to lower mean integrated squared error (MISE) compared to the current state-of-the-art.

Suggested Citation

  • Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2023. "Bandwidth selection for nonparametric regression with errors-in-variables," LSE Research Online Documents on Economics 115551, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:115551
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    References listed on IDEAS

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    More about this item

    Keywords

    measurement error models; deconvolution; nonparametric regression; bandwidth selection;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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