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Local stationarity in small area estimation models

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  • Roberto Benedetti
  • Monica Pratesi
  • Nicola Salvati

Abstract

Small area estimators are often based on linear mixed models under the assumption that relationships among variables are stationary across the area of interest (Fay–Herriot models). This hypothesis is patently violated when the population is divided into heterogeneous latent subgroups. In this paper we propose a local Fay–Herriot model assisted by a Simulated Annealing algorithm to identify the latent subgroups of small areas. The value minimized through the Simulated Annealing algorithm is the sum of the estimated mean squared error (MSE) of the small area estimates. The technique is employed for small area estimates of erosion on agricultural land within the Rathbun Lake Watershed (IA, USA). The results are promising and show that introducing local stationarity in a small area model may lead to useful improvements in the performance of the estimators. Copyright Springer-Verlag 2013

Suggested Citation

  • Roberto Benedetti & Monica Pratesi & Nicola Salvati, 2013. "Local stationarity in small area estimation models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 81-95, March.
    • Handle: RePEc:spr:stmapp:v:22:y:2013:i:1:p:81-95
    DOI: 10.1007/s10260-012-0208-1
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    References listed on IDEAS

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    5. Maria Rita Sebastiani, 2003. "Markov random‐field models for estimating local labour markets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(2), pages 201-211, May.
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