Arbeitspapier

IV methods for Tobit models

This paper studies models of processes generating censored outcomes with endogenous explanatory variables and instrumental variable restrictions. Tobit-type left censoring at zero is the primary focus in the exposition. The models studied here are unrestrictive relative to others widely used in practice, so they are relatively robust to misspeciÖcation. The models do not specify the process determining endogenous explanatory variables and they do not embody restrictions justifying control function approaches. The models can be partially or point identifying. IdentiÖed sets are characterized and it is shown how inference can be performed on scalar functions of partially identiÖed parameters when exogenous variables have rich support. In an application using data on UK household tobacco expenditures inference is conducted on the coeffi cient of an endogenous total expenditure variable with and without a Gaussian distributional restriction on the unobservable and compared with the results obtained using a point identifying complete triangular model.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP26/21

Klassifikation
Wirtschaft
Econometric and Statistical Methods and Methodology: General
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
Model Construction and Estimation
Thema
censored models
endogeneity
incomplete models
instrumental variables
partial identiÖcation
random sets

Ereignis
Geistige Schöpfung
(wer)
Chesher, Andrew
Kim, Dongwoo
Rosen, Adam M.
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2021

DOI
doi:10.47004/wp.cem.2021.2621
Handle
Letzte Aktualisierung
10.03.2025, 11:43 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
  • Kim, Dongwoo
  • Rosen, Adam M.
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2021

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