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How to interpret interaction effect in Tobit model?
Yours would be the right interpretation for the marginal effect on the latent or desired spending y ∗, but not for the censored versions of y.
Are there any marginal effects in the Tobit model?
With the Tobit, there are at least four types of marginal effects that may be interesting. You can think of latent as desired and censored as actual or observed. If negative charity spending does not quite make sense to you, it’s just a consequence of normalization.
What can multilevel Tobit models be used for?
Tobit models, whether multilevel or one-level, can be used for two types of inference—for the entire population as if it were not censored and for the censored population. We have been hired to analyze data on attendance at 500 soccer stadiums. The data are censored when the stadium is sold out.
Is the Tobit model sensitive to departures from normality?
Finally, some caveats and advice. The Tobit is extremely sensitive to departures from normality and heteroskedasticity of the errors. You can and should test these. It also makes a strong assumption that the zeros are created by the same process at the positives, which is often false.
How to predict apt using the Tobit model?
Below we run the tobit model, using read, math, and prog to predict apt. The ul ( ) option in the tobit command indicates the value at which the right-censoring begins (i.e., the upper limit). There is also a ll ( ) option to indicate the value of the left-censoring (the lower limit) which was not needed in this example.
Which is a statistically significant coefficient in Tobit?
The coefficients for read and math are statistically significant, as is the coefficient for prog =3. Tobit regression coefficients are interpreted in the similiar manner to OLS regression coefficients; however, the linear effect is on the uncensored latent variable, not the observed outcome.
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