How measurement error affects inference in linear regression

Datos Bibliográficos

ID21542007
AutoresErik Meijer (0000-0001-7959-4670, University of Southern California), Edward Oczkowski (0000-0003-2071-7116, Charles Sturt University), Tom Wansbeek (0000-0003-1365-3310, University of Groningen, autor de correspondencia)
Año2021
Volumen60
Número1
Páginas131-155
Fecha de publicación2021-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEmpirical Economics (JOURNAL)
Identificadores de la revistaISSN: 0377-7332 • E-ISSN: 1435-8921
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s00181-020-01942-z
OpenAlexW3089835505
IdiomaEN
Citas recibidas6
Referencias citadas24

Measurement error biases OLS results. When the measurement error variance in absolute or relative (reliability) form is known, adjustment is simple. We link the (known) estimators for these cases to GMM theory and provide simple derivations of their standard errors. Our focus is on the test statistics. We show monotonic relations between the t -statistics and $$R^2$$ R 2 s of the (infeasible) estimator if there was no measurement error, the inconsistent OLS estimator, and the consistent estimator that corrects for measurement error and show the relation between the t -value and the magnitude of the assumed measurement error variance or reliability. We also discuss how standard errors can be computed when the measurement error variance or reliability is estimated, rather than known, and we indicate how the estimators generalize to the panel data context, where we have to deal with dependency among observations. By way of illustration, we estimate a hedonic wine price function for different values of the reliability of the proxy used for the wine quality variable

Econometrics · Errors-in-Variables Models · Estimator · Linear regression · Observational error · Standard error · Statistics · Computer Science · Fermentation and Sensory Analysis · Horticultural and Viticultural Research · Mathematics · Wine Industry and Tourism