Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/284141 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
cemmap working paper No. CWP17/23
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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.
Schlagwörter: 
Feedback
Panel Data
Incidental Parameters
Partial Identification
JEL: 
C23
C33
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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