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Can Calibration Be Used to Adjust for “Nonignorable” Nonresponse?

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  • Kott, Phillip S.
  • Chang, Ted

Abstract

Calibration can be used to adjust for unit nonresponse when the model variables on which the response/nonresponse mechanism depends do not coincide with the benchmark variables in the calibration equation. As a result, model-variable values need only known for the respondents. This allows the treatment of what is usually considered nonignorable nonresponse. Although one can invoke either quasirandomization or prediction-model-based theory to justify the calibration, both frameworks rely on unverifiable model assumptions, and both require large samples to produce nearly unbiased estimators even when those assumptions hold. We will explore these issues theoretically and with an empirical study.

Suggested Citation

  • Kott, Phillip S. & Chang, Ted, 2008. "Can Calibration Be Used to Adjust for “Nonignorable” Nonresponse?," NASS Research Reports 234387, United States Department of Agriculture, National Agricultural Statistics Service.
    • Handle: RePEc:ags:unasrr:234387
    DOI: 10.22004/ag.econ.234387
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    References listed on IDEAS

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    1. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
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    Cited by:

    1. Puying Zhao & Hui Zhao & Niansheng Tang & Zhaohai Li, 2017. "Weighted composite quantile regression analysis for nonignorable missing data using nonresponse instrument," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 189-212, April.
    2. Maria Michela Dickson & Giuseppe Espa & Lorenzo Fattorini & Flavio Santi, 2022. "Double-calibration estimators accounting for under-coverage and nonresponse in socio-economic surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1273-1288, December.

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