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Looking at the above histogram showing the distribution of apt , we can see the censoring in the data, that is, there are far more cases with scores of to than one would expect looking at the rest of the distribution. In the histogram below, the discrete option produces a histogram where each unique value of apt has its own bar.
The freq option causes the y-axis to be labeled with the frequency for each value, rather than the density. 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. In the last row of the scatterplot matrix shown above, we see the scatterplots showing read and apt , as well as math and apt.
Note the collection of cases at the top of each scatterplot 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 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. Next we correlate the observed values of apt with the predicted values yhat. The correlation between the predicted and observed values of apt is 0. Additionally, we can use the user-written command fitstat to produce a variety of fit statistics. You can find more information on fitstat by typing search fitstat see How can I use the search command to search for programs and get additional help?
It happens like this because you are specifying a linear prediction, but from your post it seems that you want nonlinear prediction. I think that to obtain the quantity that I think you want, you need to do: predict double xb, e 0,1. Comment Post Cancel. Read -tobit postestimation- to see the available options for -predict-. Thanks Joro Kolev for suggesting the reading "tobit postestimation". I tried with predict double xb, ystar 0,1 and the values matches with the predicted values obtained from other statistical package, however, I prefer Stata results due to certain flexibility available in it.
It simplified my task. I hope the above estimations are correct. Dimitriy V. There are 4 possible things you can predict after fitting the tobit. You get the prediction for the subpopulation that is on the interior i. The prediction for the censored expected value is ystar 0,1 , which will also include the 0s and the 1s. Masterov Thanks. Previous Next. Yes No.
Hi all! I am running the tobit model on stata with the following command: tobit effscr lnta_round firmage forex freecashflow. ivprob and ivtobit are used for estimating probit models where one or more of the independent variables is endogenous. Neither weights nor the robust option are. Estimates a tobit model and provides a table of marginal effects evaluated at the observed censoring rate of the dependent variable. The.