Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/189749 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
cemmap working paper No. CWP34/18
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold-crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.
Schlagwörter: 
Identification
selection
multivalued treatments
instruments
monotonicity
multidimensional unobserved heterogeneity
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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