Misclassification in binary choice models
Result description
Bias from misclassification of binary dependent variables can be pronounced. We examine what can be learned from such contaminated data. First, we derive the asymptotic bias in parametric models allowing misclassification to be correlated with observables and unobservables. Simulations and validation data show that the bias formulas are accurate in finite samples and in most situations imply attenuation. Second, we examine the bias in a prototypical application. Erroneously restricting the covariance of misclassification and covariates aggravates the bias for all estimators we examine. Estimators that relax this restriction perform well if a model of misclassification or validation data is available.
Keywords
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/00216208:11640/17:00478019
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Misclassification in binary choice models
Original language description
Bias from misclassification of binary dependent variables can be pronounced. We examine what can be learned from such contaminated data. First, we derive the asymptotic bias in parametric models allowing misclassification to be correlated with observables and unobservables. Simulations and validation data show that the bias formulas are accurate in finite samples and in most situations imply attenuation. Second, we examine the bias in a prototypical application. Erroneously restricting the covariance of misclassification and covariates aggravates the bias for all estimators we examine. Estimators that relax this restriction perform well if a model of misclassification or validation data is available.
Czech name
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Czech description
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Classification
Type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50201 - Economic Theory
Result continuities
Project
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Econometrics
ISSN
0304-4076
e-ISSN
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Volume of the periodical
200
Issue of the periodical within the volume
2
Country of publishing house
CH - SWITZERLAND
Number of pages
17
Pages from-to
295-311
UT code for WoS article
000410870600012
EID of the result in the Scopus database
2-s2.0-85026309230
Basic information
Result type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
OECD FORD
Economic Theory
Year of implementation
2017