One of the main motivations of concern when we apply a small area es- timation model is to relate individual area estimates with some direct estimates in a larger area. External and internal benchmarked estima- tors provide adjusted model-based estimates, in order to agree with that aggregated results. The use of multiple calibration quantities in the bench- marking matrix suggests that the underlying “true” model is misspecified by the actual model equation. We examine the appropriateness of em- ploying the benchmarking matrix to account for omitted variables in the model, through an additional regression term.

On benchmarking small area estimators when the model is misspecified

Laura Marcis;Renato Salvatore
2021-01-01

Abstract

One of the main motivations of concern when we apply a small area es- timation model is to relate individual area estimates with some direct estimates in a larger area. External and internal benchmarked estima- tors provide adjusted model-based estimates, in order to agree with that aggregated results. The use of multiple calibration quantities in the bench- marking matrix suggests that the underlying “true” model is misspecified by the actual model equation. We examine the appropriateness of em- ploying the benchmarking matrix to account for omitted variables in the model, through an additional regression term.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/95687
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