Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/247604 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 9 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-25
Verlag: 
MDPI, Basel
Zusammenfassung: 
We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on the complementarity of instrumental variable (IV) and latent factor methods and how this complementarity depends on the number of outcome variables and the degree of contamination in the IV. We apply the causal inference methods to assess the impact of mental illness on work absenteeism and disability, using the National Comorbidity Survey Replication.
Schlagwörter: 
disability
instrumental variable
latent factor models
mental illness
treatment effect
JEL: 
C3
I12
J210
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