Evaluating the maximum MSE of mean estimators with missing data
Charles F. Manski
Department of Economics
Northwestern University
Evanston, IL
[email protected]
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Max Tabord-Meehan
Department of Economics
Northwestern University
Evanston, IL
[email protected]
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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.
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Charles F. Manski, Max Tabord-Meehan
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wald_mse, maximum mean squared error
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