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- W4320501088 abstract "We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given parametric specification while other structural features of the model are maintained. Our approach recasts the infinite-dimensional problem of optimizing the counterfactual with respect to the distribution of latent variables (subject to model constraints) as a finite-dimensional convex program. We also develop an MPEC version of our method to further simplify computation in models with endogenous parameters (e.g., value functions) defined by equilibrium constraints. We propose plug-in estimators of the bounds and two methods for inference. We also show that our bounds converge to the sharp nonparametric bounds on counterfactuals as the neighborhood size becomes large. To illustrate the broad applicability of our procedure, we present empirical applications to matching models with transferable utility and dynamic discrete choice models." @default.
- W4320501088 created "2023-02-14" @default.
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- W4320501088 date "2019-04-01" @default.
- W4320501088 modified "2023-09-30" @default.
- W4320501088 title "Counterfactual Sensitivity and Robustness" @default.
- W4320501088 doi "https://doi.org/10.48550/arxiv.1904.00989" @default.
- W4320501088 hasPublicationYear "2019" @default.
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