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<p>Small area estimators based on area-level random effect models are popular. When the normality assumption fails for random effects, the properties of the estimators deteriorate. In these cases, robust versions of small area predictors are useful. As an alternative to robust empirical best linear unbiased predictors, we propose an extension of M-quantile small-area methods to area-level models. We apply our methodology to estimate the mean equivalized income for local labour systems in Italy via data from the EU-SILC survey. The advantages of the proposed technique are demonstrated in the application and in a simulation exercise.</p>

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Small area estimation of equivalized income for local labour systems in Italy via M-quantile area-level models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月10日

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  • Stefano Marchetti,
  • Nicola Salvati,
  • Enrico Fabrizi,
  • Nikos Tzavidis

摘要

Small area estimators based on area-level random effect models are popular. When the normality assumption fails for random effects, the properties of the estimators deteriorate. In these cases, robust versions of small area predictors are useful. As an alternative to robust empirical best linear unbiased predictors, we propose an extension of M-quantile small-area methods to area-level models. We apply our methodology to estimate the mean equivalized income for local labour systems in Italy via data from the EU-SILC survey. The advantages of the proposed technique are demonstrated in the application and in a simulation exercise.