Testing for the presence of measurement error in Stata

Young Jun Lee () and Daniel Wilhelm
Additional contact information
Young Jun Lee: University of Copenhagen

Stata Journal, 2020, vol. 20, issue 2, 382-404

Abstract: In this article, we describe how to test for the presence of measure- ment error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new com- mand, dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fis- cal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.

Keywords: dgmtest; nonparametric test; measurement error; measurement error bias (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0600 link to article purchase
http://www.stata-journal.com/software/sj20-2/st0600/ (text/html)

Related works:
Working Paper: Testing for the presence of measurement error in Stata (2019)
Working Paper: Testing for the presence of measurement error in Stata (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:20:y:2019:i:2:p:382-404

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

DOI: 10.1177/1536867X20931002

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().