Identification in a binary choice panel data model with a predetermined covariate

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP01/23

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
• Thema: Sequential Moment Conditions ; Feedback ; Panel Data ; Incidental Parameters ; Partial Identification

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Bonhomme, Stéphane ; Dano, Kevin ; Graham, Bryan S.
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2023

Weitere Informationen

• DOI: doi:10.47004/wp.cem.2023.0123
• Handle: http://hdl.handle.net/10419/272837
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ