Artikel
Robust inference in deconvolution
Kotlarski's identity has been widely used in applied economic research based on repeated-measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results.
- Language
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Englisch
- Bibliographic citation
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Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 12 ; Year: 2021 ; Issue: 1 ; Pages: 109-142 ; New Haven, CT: The Econometric Society
- Classification
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Wirtschaft
Semiparametric and Nonparametric Methods: General
Econometrics of Games and Auctions
- Subject
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Deconvolution
measurement error
robust inference
uniform confidence band
- Event
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Geistige Schöpfung
- (who)
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Kato, Kengo
Sasaki, Yuya
Ura, Takuya
- Event
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Veröffentlichung
- (who)
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The Econometric Society
- (where)
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New Haven, CT
- (when)
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2021
- DOI
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doi:10.3982/QE1643
- Handle
- Last update
-
10.03.2025, 11:45 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
- Artikel
Associated
- Kato, Kengo
- Sasaki, Yuya
- Ura, Takuya
- The Econometric Society
Time of origin
- 2021
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