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Tuesday, October 19, 2021 at 11:15am to 12:45pm
Ives Hall, 217
Ben Deaner, Cowles College
Proxy Controls and Panel Data-Paper is in draft
Abstract: We present a flexible approach to estimation and inference in nonparametric, non-separable models using `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but are informative proxies for variables that do. Our analysis applies to cross-sectional settings but is particularly well-suited to panel models. Our identification results motivate a simple and `well-posed' nonparametric estimator. We derive convergence rates for the estimator and construct uniform confidence bands with asymptotically correct size. In panel settings, our methods provide a novel approach to the difficult problem of identification with non-separable, general heterogeneity and fixed $T$. In panels, observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure. We apply our methodology to two empirical settings. We estimate causal effects of grade retention on cognitive performance and we estimate consumer demand counterfactuals using panel data.
Cornell Economics Commnity (List Service Mambers)
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