The Tobit Model with a Non-Zero Threshold

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Show simple item record Carson, R Sun, Y 2012-02-02T11:02:32Z 2007-01
dc.identifier.citation The Econometrics Journal, 2007, 10 (3), pp. 488 - 502
dc.identifier.issn 1368-4221
dc.identifier.other C1UNSUBMIT en_US
dc.description.abstract The standard Tobit maximum likelihood estimator under zero censoring threshold produces inconsistent parameter estimates, when the constant censoring threshold γ is nonzero and unknown. Unfortunately, the recording of a zero rather than the actual censoring threshold value is typical of economic data. Non-trivial minimum purchase prices for most goods, fixed cost for doing business or trading, social customs such as those involving charitable donations, and informal administrative recording practices represent common examples of nonzero constant censoring threshold where the constant threshold is not readily available to the econometrician. Monte Carlo results show that this bias can be extremely large in practice. A new estimator is proposed to estimate the unknown censoring threshold. It is shown that the estimator is superconsistent and follows an exponential distribution in large samples. Due to the superconsistency, the asymptotic distribution of the maximum likelihood estimator of other parameters is not affected by the estimation uncertainty of the censoring threshold.
dc.publisher Wiley-Blackwell Publishing Ltd.
dc.relation.isbasedon 10.1111/j.1368-423X.2007.00218.x
dc.title The Tobit Model with a Non-Zero Threshold
dc.type Journal Article
dc.parent The Econometrics Journal
dc.journal.volume 3
dc.journal.volume 10
dc.journal.number 3 en_US
dc.publocation United Kingdom en_US
dc.identifier.startpage 488 en_US
dc.identifier.endpage 502 en_US BUS.Faculty of Business en_US
dc.conference Verified OK en_US
dc.for 1403 Econometrics
dc.personcode 100722
dc.percentage 100 en_US Econometrics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords Exponential distribution, Maximum likelihood, Order statistic, Threshold determination. en_US
dc.description.keywords Exponential distribution, Maximum likelihood, Order statistic, Threshold determination.
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Business
pubs.organisational-group /University of Technology Sydney/Strength - Study of Choice
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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