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

Stéphane Bonhomme, Kevin Dano and Bryan Graham

No 31027, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C23 (search for similar items in EconPapers)
Date: 2023-03
New Economics Papers: this item is included in nep-dcm
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Published as Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2023. "Identification in a binary choice panel data model with a predetermined covariate," SERIEs, vol 14(3-4), pages 315-351.

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