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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

measurement errorbinary choice modelsprogram take-up

The result's identifiers

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

  • Czech description

Classification

  • Type

    Jimp - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50201 - Economic Theory

Result continuities

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

  • 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

Jimp

OECD FORD

Economic Theory

Year of implementation

2017