Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253609 
Year of Publication: 
2021
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 12 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 109-142
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
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.
Subjects: 
Deconvolution
measurement error
robust inference
uniform confidence band
JEL: 
C14
C57
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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