Testing for the presence of measurement error in Stata

Young Jun Lee, Daniel Wilhelm

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 (2018). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.

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Year of publication:	[2019]
Authors:	Lee, Young Jun ; Wilhelm, Daniel
Publisher:	[London] : Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL
Subject:	st0001 \| nonparametric test \| measurement error \| measurement errorbias \| Statistischer Fehler \| Statistical error \| Nichtparametrischer Test \| Nonparametric test \| Einkommensstatistik \| Income statistics \| USA \| United States

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Extent:	1 Online-Ressource (circa 23 Seiten) Illustrationen
Series:	CEMMAP working papers / Centre for Microdata Methods and Practice. - London : [Verlag nicht ermittelbar], ISSN 1753-9196, ZDB-ID 2106928-1. - Vol. CWP19, 47
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Graue Literatur ; Non-commercial literature ; Arbeitspapier ; Working Paper
Language:	English
Other identifiers:	10.1920/wp.cem.2019.4719 [DOI] hdl:10419/211140 [Handle]
Source:	ECONIS - Online Catalogue of the ZBW

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