Identification in a binary choice panel data model with a predetermined covariate

Stéphane Bonhomme, Kevin Dano and Bryan S. Graham

No 17/23, CeMMAP working papers from Institute for Fiscal Studies

Abstract: We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter θ, 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 θ 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 θ and show how to compute it using linear programming techniques. While θ is not generally point-identified, its identified set is informative in the examples we analyze numerically, suggesting that meaningful learning about θ may be possible even in short panels with feedback. As a complement, we report calculations of identified sets for an average partial effect, and find informative sets in this case as well.

Date: 2023-07-26
New Economics Papers: this item is included in nep-dcm and nep-des
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:17/23

DOI: 10.47004/wp.cem.2023.1723

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