Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/240391 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
CHCP Working Paper No. 2020-2
Verlag: 
The University of Western Ontario, Centre for Human Capital and Productivity (CHCP), London (Ontario)
Zusammenfassung: 
This paper studies nonparametric identification and counterfactual bounds for heterogeneous firms that can be ranked in terms of productivity. Our approach works when quantities and prices are latent rendering standard approaches inapplicable. Instead, we require observation of profits or other optimizing-values such as costs or revenues, and either prices or price proxies of flexibly chosen variables. We extend classical duality results for price-taking firms to a setup with discrete heterogeneity, endogeneity, and limited variation in possibly latent prices. Finally, we show that convergence results for nonparametric estimators may be directly converted to convergence results for production sets.
Schlagwörter: 
Counterfactual bounds
cost minimization
nonseparable heterogeneity
partial identification
profit maximization
production set
revenuemaximization
shape restrictions
JEL: 
C5
D24
Dokumentart: 
Working Paper

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