UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

IV methods for Tobit models

Chesher, Andrew; Kim, Dongwoo; Rosen, Adam M; (2023) IV methods for Tobit models. Journal of Econometrics , 235 (2) pp. 1700-1724. 10.1016/j.jeconom.2023.01.010.

[thumbnail of IVTobitFinalRevision.pdf] Text
IVTobitFinalRevision.pdf - Other
Access restricted to UCL open access staff until 16 June 2025.

Download (477kB)


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. Extension to stochastic censoring is sketched. The models do not specify the process determining endogenous explanatory variables and they do not embody restrictions justifying control function approaches. Consequently, they can be partially or point identifying. Identified sets are characterized and it is shown how inference can be performed on scalar functions of partially identified parameters when exogenous variables have rich support. In an application using data on UK household tobacco expenditures inference is conducted on the coefficient 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.

Type: Article
Title: IV methods for Tobit models
DOI: 10.1016/j.jeconom.2023.01.010
Publisher version: https://doi.org/10.1016/j.jeconom.2023.01.010
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Censored outcomes, Endogeneity, Incomplete models, Instrumental variables, Partial identification, Stochastic censoring
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10165211
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item