Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/272837 
Year of Publication: 
2023
Series/Report no.: 
cemmap working paper No. CWP01/23
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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 is possible even in short panels with feedback.
Subjects: 
Sequential Moment Conditions
Feedback
Panel Data
Incidental Parameters
Partial Identification
JEL: 
C23
C33
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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