Identification in a Binary Choice Panel Data Model with a Predetermined Covariate

arXiv2301.05733MaRDI QIDQ6423358

Author name not available (Why is that?)

Publication date: 13 January 2023

Abstract: We study identification in a binary choice panel data model with a single emph{predetermined} binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter $heta$, whereas the distribution of unit-specific heterogeneity, as well as the feedback process that maps lagged outcomes into future covariate realizations, are left unrestricted. We provide a simple condition under which $heta$ is never point-identified, no matter the number of time periods available. This condition is satisfied in most models, including the logit one. We also characterize the identified set of $heta$ and show how to compute it using linear programming techniques. While $heta$ is not generally point-identified, its identified set is informative in the examples we analyze numerically, suggesting that meaningful learning about $heta$ is possible even in short panels with feedback.

Has companion code repository: https://github.com/kevindano/bonhomme-dano-graham-series

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