Why use tobit

Because apt is continuous, most values of apt are unique in the dataset, although close to the center of the distribution there are a few values of apt that have two or three cases.

Next we'll explore the bivariate relationships in our dataset. In the first row of the scatterplot matrix shown above, we see the scatterplots showing the relationship between read and apt , as well as math and apt. Note the collection of cases at the top these two scatterplots, this is due to the censoring in the distribution of apt. Below is a list of some analysis methods you may have encountered. Some of the methods listed are quite reasonable while others have either fallen out of favor or have limitations.

Below we run the tobit model, using the vglm function of the VGAM package. Below we calculate the p-values for each of the coefficients in the model. We calculate the p-value for each coefficient using the z values and then display in a table with the coefficients.

We can test the significant of program type overall by fitting a model without program in it and using a likelihood ratio test. The LRT with two degrees of freedom is associated with a p-value of 0. We may also wish to examine how well our model fits the data. Comment Post Cancel. John Mullahy. Prateek: Given your data's structure I strongly endorse Clyde's recommendation to avoid Tobit.

Clyde Schechter and John Mullahy : Thanks a lot for your very helpful clarifications and insights. I shall now explore fractional regression technique. Have a great day! Hello Clyde Schechter and John Mullahy. I did find relevant commands like glm, fracreg, betareg and fracglm which consider the possibility of a fractional dependent variable.

However, I could not find an appropriate command for running fractional regressions in case of panel dataset balanced or unbalanced. However, if the conditional distribution had non-uniform variance, then the Tobit model performed at least as poorly as the OLS model. Abstract Self-reported health status is often measured using psychometric or utility indices that provide a score intended to summarize an individual's health. 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. There is also a ll option to indicate the value of the left-censoring the lower limit which was not needed in this example.

The i. Note that this syntax was introduced in Stata We can test for an overall effect of prog using the test command. Below we see that the overall effect of prog is statistically significant. We can also test additional hypotheses about the differences in the coefficients for different levels of prog. We may also wish to see measures of how well our model fits.

This can be particularly useful when comparing competing models. One method of doing this is to compare the predicted values based on the tobit model to the observed values in the dataset. Below we use predict to generate predicted values of apt based on the model.