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Evaluating the maximum MSE of mean estimators with missing data

Author

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  • Charles F. Manski

    (Northwestern University)

  • Max Tabord-Meehan

    (Northwestern University)

Abstract

In this article, we present the wald mse command, which computes the maximum mean squared error of a user-specified point estimator of the mean for a population of interest in the presence of missing data. As pointed out by Manski (1989, Journal of Human Resources 24: 343–360; 2007, Journal of Econometrics 139: 105–115), the presence of missing data results in the loss of point identification of the mean unless one is willing to make strong assumptions about the nature of the missing data. Despite this, decision makers may be interested in reporting a single number as their estimate of the mean as opposed to an estimate of the identified set. It is not obvious which estimator of the mean is best suited to this task, and there may not exist a universally best choice in all settings. To evaluate the performance of a given point estimator of the mean, wald mse allows the decision maker to compute the maximum mean squared error of an arbitrary estimator under a flexible specification of the missing-data process.

Suggested Citation

  • Charles F. Manski & Max Tabord-Meehan, 2017. "Evaluating the maximum MSE of mean estimators with missing data," Stata Journal, StataCorp LP, vol. 17(3), pages 723-735, September.
    • Handle: RePEc:tsj:stataj:y:17:y:2017:i:3:p:723-735
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    Cited by:

    1. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    2. Charles F. Manski, 2019. "Statistical inference for statistical decisions," Papers 1909.06853, arXiv.org.

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    Keywords

    wald mse; maximum mean squared error;

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