Arbeitspapier
Semiparametric estimation of structural functions in nonseparable triangular models
This paper introduces two classes of semiparametric triangular systems with nonadditively separable unobserved heterogeneity. They are based on distribution and quantile regression modeling of the reduced-form conditional distributions of the endogenous variables. We show that these models are flexible and identify the average, distribution and quantile structural functions using a control function approach that does not require a large support condition. We propose a computationally attractive three-stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. We illustrate the implementation and applicability of our methods with numerical simulations and an empirical application to demand analysis.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP48/17
- Classification
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Wirtschaft
- Subject
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Structural functions
nonseparable models
control function
quantile and distribution regression
semiparametric estimation
uniform inference
- Event
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Geistige Schöpfung
- (who)
-
Chernozhukov, Victor
Fernández-Val, Iván
Newey, Whitney
Stouli, Sami
Vella, Francis
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2017
- DOI
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doi:10.1920/wp.cem.2017.4817
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
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
- Arbeitspapier
Associated
- Chernozhukov, Victor
- Fernández-Val, Iván
- Newey, Whitney
- Stouli, Sami
- Vella, Francis
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2017
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