Artikel
Misclassification in binary choice models with sample selection
Most empirical work in the social sciences is based on observational data that are often both incomplete, and therefore unrepresentative of the population of interest, and affected by measurement errors. These problems are very well known in the literature and ad hoc procedures for parametric modeling have been proposed and developed for some time, in order to correct estimate's bias and obtain consistent estimators. However, to our best knowledge, the aforementioned problems have not yet been jointly considered. We try to overcome this by proposing a parametric approach for the estimation of the probabilities of misclassification of a binary response variable by incorporating them in the likelihood of a binary choice model with sample selection.
- Language
-
Englisch
- Bibliographic citation
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 7 ; Year: 2019 ; Issue: 3 ; Pages: 1-19 ; Basel: MDPI
- Classification
-
Wirtschaft
- Subject
-
misclassified dependent variable
sample selection bias
undeclared work
- Event
-
Geistige Schöpfung
- (who)
-
Arezzo, Maria Felice
Guagnano, Giuseppina
- Event
-
Veröffentlichung
- (who)
-
MDPI
- (where)
-
Basel
- (when)
-
2019
- DOI
-
doi:10.3390/econometrics7030032
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Arezzo, Maria Felice
- Guagnano, Giuseppina
- MDPI
Time of origin
- 2019
Other Objects (12)
Semiparametric Estimation of a Binary Choice Model with Sample Selection
Response-based sampling for binary choice models with sample selection
Bandwidth selection for treatment choice with binary outcomes
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis
Construction of cost sensitive binary classification rules accounting for measurement and uncertain misclassification costs
Predictions from Binary Choice Models
Dynamic time series binary choice
Correcting for misclassification and selection effects in estimating net survival in clinical trials
Identification in dynamic binary choice models
Binary Choice Probabilities on Mixture Sets
Identification in dynamic binary choice models
Semiparametric Estimation of a Binary Choice Model with Sample Selection
Response-based sampling for binary choice models with sample selection
Bandwidth selection for treatment choice with binary outcomes
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis
Construction of cost sensitive binary classification rules accounting for measurement and uncertain misclassification costs
Predictions from Binary Choice Models
Dynamic time series binary choice
Correcting for misclassification and selection effects in estimating net survival in clinical trials
Identification in dynamic binary choice models
Binary Choice Probabilities on Mixture Sets
Identification in dynamic binary choice models
Semiparametric Estimation of a Binary Choice Model with Sample Selection
Response-based sampling for binary choice models with sample selection
Bandwidth selection for treatment choice with binary outcomes
Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs
Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis
Construction of cost sensitive binary classification rules accounting for measurement and uncertain misclassification costs
Predictions from Binary Choice Models
Dynamic time series binary choice
Correcting for misclassification and selection effects in estimating net survival in clinical trials
Identification in dynamic binary choice models
Binary Choice Probabilities on Mixture Sets