Dear all,
I have a question regarding statistical models. I have a panel data
with obs on people's background information (age, wealth from other
sources, etc), and wage if the person decides to work. A person may
choose to work, if so, there is an observable wage; if not, wage is 0.
I am confused about what models to use. It looks like a standard tobit
model, with wage censored at 0. Below is an alternative way to model.
A: Probit model of the decision to work or not.
B: For wage>0 observations, use OLS regression.
I have seen both in practice. But while using two models, the authors
mention that they are estimating both model A and model B at the same
time. What does it mean "at the same time"?
Bottom line:
If both tobit and the joint estimation of models A and B are
appropriate, which is better?
Thanks for clarifying this for me!
Faye
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