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Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
cemmap working paper No. CWP24/23
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to further gains when the number of outcomes grows. We illustrate this via simulation and in a re-analysis of the impact of the Flint water crisis on educational outcomes.
Schlagwörter: 
panel data
synthetic control method
linear factor model
JEL: 
C13
C21
C23
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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