Testing for the presence of measurement error in Stata

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Standard

Testing for the presence of measurement error in Stata. / Lee, Young Jun; Wilhelm, Daniel.

I: Stata Journal, Bind 20, Nr. 2, 06.2020, s. 382-404.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Lee, YJ & Wilhelm, D 2020, 'Testing for the presence of measurement error in Stata', Stata Journal, bind 20, nr. 2, s. 382-404. https://doi.org/10.1177/1536867X20931002

APA

Lee, Y. J., & Wilhelm, D. (2020). Testing for the presence of measurement error in Stata. Stata Journal, 20(2), 382-404. https://doi.org/10.1177/1536867X20931002

Vancouver

Lee YJ, Wilhelm D. Testing for the presence of measurement error in Stata. Stata Journal. 2020 jun.;20(2):382-404. https://doi.org/10.1177/1536867X20931002

Author

Lee, Young Jun ; Wilhelm, Daniel. / Testing for the presence of measurement error in Stata. I: Stata Journal. 2020 ; Bind 20, Nr. 2. s. 382-404.

Bibtex

@article{ab60937b65544cfabce408bbde185976,
title = "Testing for the presence of measurement error in Stata",
abstract = "In this article, 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 command,dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal 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 = "st0600, dgmtest, nonparametric test, measurement error, measurement error bias, NONLINEAR MODELS, NONPARAMETRIC-ESTIMATION, INSTRUMENTAL VARIABLES, SPECIFICATION TESTS, MISCLASSIFICATION, IDENTIFICATION, DYNAMICS, MICRO",
author = "Lee, {Young Jun} and Daniel Wilhelm",
year = "2020",
month = jun,
doi = "10.1177/1536867X20931002",
language = "English",
volume = "20",
pages = "382--404",
journal = "Stata Journal",
issn = "1536-867X",
publisher = "Stata Press",
number = "2",

}

RIS

TY - JOUR

T1 - Testing for the presence of measurement error in Stata

AU - Lee, Young Jun

AU - Wilhelm, Daniel

PY - 2020/6

Y1 - 2020/6

N2 - In this article, 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 command,dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.

AB - In this article, 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 command,dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.

KW - st0600

KW - dgmtest

KW - nonparametric test

KW - measurement error

KW - measurement error bias

KW - NONLINEAR MODELS

KW - NONPARAMETRIC-ESTIMATION

KW - INSTRUMENTAL VARIABLES

KW - SPECIFICATION TESTS

KW - MISCLASSIFICATION

KW - IDENTIFICATION

KW - DYNAMICS

KW - MICRO

U2 - 10.1177/1536867X20931002

DO - 10.1177/1536867X20931002

M3 - Journal article

VL - 20

SP - 382

EP - 404

JO - Stata Journal

JF - Stata Journal

SN - 1536-867X

IS - 2

ER -

ID: 255045612