Computation of Standard Errors

Authors

  • Bryan E. Dowd Ph.D,

    Corresponding author
    1. Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN 55455
    • Address correspondence to Bryan E. Dowd, Ph.D., Division of Health Policy and Management, School of Public Health, University of Minnesota, Box 729 MMC, Minneapolis, MN 55455; e-mail: dowdx001@umn.edu.

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  • William H. Greene Ph.D,

    1. Stern School of Business, Kaufman Management Center, New York, NY
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  • Edward C. Norton Ph.D

    1. Department of Health Management and Policy, Department of Economics, M3108 SPH II, Ann Arbor, MI
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Abstract

Objectives

We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages.

Study Design

We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects.

Empirical Application

Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5.

Conclusions

In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values.

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