Arbeitspapier

Identification analysis in models with unrestricted latent variables: Fixed effects and initial conditions

Many structural econometric models include latent variables on whose probability distributions one may wish to place minimal restrictions. Leading examples in panel data models are individual-specific variables sometimes treated as "fixed effects" and, in dynamic models, initial conditions. This paper presents a generally applicable method for characterizing sharp identified sets when models place no restrictions on the probability distribution of certain latent variables and no restrictions on their covariation with other variables. Endogenous explanatory variables can be easily accommodated. Examples of application to some static and dynamic binary, ordered and multiple discrete choice panel data models are presented.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP20/23

Klassifikation
Wirtschaft
Thema
Panel
Variable
Statistical distribution
Modeling
Panel
Variable
Statistische Verteilung
Modellierung

Ereignis
Geistige Schöpfung
(wer)
Chesher, Andrew
Rosen, Adam M.
Zhang, Yuanqi
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2023

DOI
doi:10.47004/wp.cem.2023.2023
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Chesher, Andrew
  • Rosen, Adam M.
  • Zhang, Yuanqi
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2023

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