Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/253609 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 12 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 109-142
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
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.
Schlagwörter: 
Deconvolution
measurement error
robust inference
uniform confidence band
JEL: 
C14
C57
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by-nc Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
442.68 kB





Publikationen in EconStor sind urheberrechtlich geschützt.