Testing for the presence of measurement error in Stata
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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
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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