A Note on Simultaneous Confidence Intervals for Direct, Indirect and Synthetic Estimators

Christophe Quentin Valvason () and Stefan Sperlich ()
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Christophe Quentin Valvason: Geneva School of Economics and Management, University of Geneva, 40 Boulevard du Pont d’Arve, 1204 Geneva, Switzerland
Stefan Sperlich: Geneva School of Economics and Management, University of Geneva, 40 Boulevard du Pont d’Arve, 1204 Geneva, Switzerland

Stats, 2024, vol. 7, issue 1, 1-17

Abstract: Direct, indirect and synthetic estimators have a long history in official statistics. While model-based or model-assisted approaches have become very popular, direct and indirect estimators remain the predominant standard and are therefore important tools in practice. This is mainly due to their simplicity, including low data requirements, assumptions and straightforward inference. With the increasing use of domain estimates in policy, the demands on these tools have also increased. Today, they are frequently used for comparative statistics. This requires appropriate tools for simultaneous inference. We study devices for constructing simultaneous confidence intervals and show that simple tools like the Bonferroni correction can easily fail. In contrast, uniform inference based on max-type statistics in combination with bootstrap methods, appropriate for finite populations, work reasonably well. We illustrate our methods with frequently applied estimators of totals and means.

Keywords: domain estimation; simultaneous confidence intervals; uniform inference; comparative statistics (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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