Logit, Probit and Tobit: Models for Categorical and Limited ...
Logit, Probit and Tobit: Models for Categorical and Limited ...
Logit, Probit and Tobit: Models for Categorical and Limited ...
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The <strong>Tobit</strong> Model<br />
• The <strong>Tobit</strong> model uses all of the in<strong>for</strong>mation,<br />
in<strong>for</strong>mation<br />
including info on censoring <strong>and</strong> provides consistent<br />
estimates.<br />
• It is also a nonlinear model <strong>and</strong> similar to the probit<br />
model. It is estimated using g maximum likelihood<br />
estimation techniques. The likelihood function <strong>for</strong><br />
the tobit model takes the <strong>for</strong>m:<br />
• This is an unusual function, it consists of two terms,<br />
the first <strong>for</strong> non-censored observations (it is the pdf),<br />
<strong>and</strong> dth the second df<strong>for</strong> censored dobservations b ti (iti (it is th the cdf).<br />
df)
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