Identification in a Binary Choice Panel Data Model with a Predetermined Covariate / Stéphane Bonhomme, Kevin Dano, Bryan S. Graham.

Format:

Book

Author/Creator:

Bonhomme, Stéphane.

Contributor:

National Bureau of Economic Research.

Dano, Kevin.

Graham, Bryan S.

Series:

Working Paper Series (National Bureau of Economic Research) no. w31027.

NBER working paper series no. w31027

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

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.

Notes:

Print version record

March 2023.