Minimax-regret sample design in anticipation of missing data, with application to panel data

Jeff Dominitz and Charles Manski

Journal of Econometrics, 2022, vol. 226, issue 1, 104-114

Abstract: Missing data problems are ubiquitous in data collection. In surveys, these problems may arise from unit response, item nonresponse, and panel attrition. Building on the Dominitz and Manski (2017) study of choice between two or more sampling processes that differ in cost and quality, we study minimax-regret sample design in anticipation of missing data, where the collected data will be used for prediction under square loss of the values of functions of two variables. The analysis imposes no assumptions that restrict unobserved outcomes. Findings are reported for prediction of the values of linear and indicator functions using panel data with attrition. We also consider choice between a panel and repeated cross sections.

Keywords: Statistical decision theory; Prediction; Partial identification; Nonresponse in surveys (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:226:y:2022:i:1:p:104-114

DOI: 10.1016/j.jeconom.2020.12.006

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