Working Paper: NBER ID: w31027
Authors: Stphane Bonhomme; Kevin Dano; Bryan S. Graham
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.
Keywords: No keywords provided
JEL Codes: C23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| failure of point identification (C52) | coefficient on a predetermined covariate (C29) |
| number of observations (C29) | width of identified sets (C55) |
| identified set provides informative insights (C55) | point identification failure (C52) |
| linear programming methods (C51) | computation of identified sets (C69) |