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CWP511818.pdf
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In this paper, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new Stata command [R] dgmtest for a nonparametric test proposed in Wilhelm (2018b). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.
Authors
![Daniel Wilhelm](/sites/default/files/styles/square_desktop/public/2022-07/Daniel%20Wilhelm.jpg?itok=CuHK3qVe)
Research Associate LMU Munich
Daniel is a Research Associate of the IFS in Cemmap and Professor of Statistics and Econometrics at LMU Munich.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Young Jun Lee
Working Paper details
- DOI
- 10.1920/wp.cem.2018.5118
- Publisher
- The IFS
Suggested citation
Jun Lee, Y and Wilhelm, D. (2018). Testing for the presence of measurement error in Stata. London: The IFS. Available at: https://ifs.org.uk/publications/testing-presence-measurement-error-stata-0 (accessed: 30 June 2024).
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