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

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  • Dominitz, Jeff
  • Manski, Charles F.

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.

Suggested Citation

  • Dominitz, Jeff & Manski, Charles F., 2022. "Minimax-regret sample design in anticipation of missing data, with application to panel data," Journal of Econometrics, Elsevier, vol. 226(1), pages 104-114.
    • Handle: RePEc:eee:econom:v:226:y:2022:i:1:p:104-114
    DOI: 10.1016/j.jeconom.2020.12.006
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    References listed on IDEAS

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    1. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
    2. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    3. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    4. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    5. Jeff Dominitz & Charles F. Manski, 2017. "More Data or Better Data? A Statistical Decision Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(4), pages 1583-1605.
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    Cited by:

    1. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2023. "Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds," Papers 2302.02988, arXiv.org, revised Jul 2023.

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