Censored Regression :: SAS(R) Studio 3.5: Task Reference Guide

Example: Censored Regression

To create this example:

Create the Work.Cigar data set. For more information, see CIGAR Data Set.
In the Tasks section, expand the Econometrics folder, and then double-click Cross-sectional Data Models. The user interface for the Cross-Sectional Data Models task opens.
On the Data tab, select the WORK.CIGAR data set.

Tip
If the data set is not available from the drop-down list, click . In the Choose a Table window, expand the library that contains the data set that you want to use. Select the data set for the example and click OK. The selected data set should now appear in the drop-down list.

Assign columns to these roles:

On the Model tab, complete these steps:
1. Select Censored regression as the model type.
2. Select the Set the lower bound check box. From the Lower bound method drop-down list, select Specify by value. In the Lower bound value box, enter 90.
On the Options tab, expand the Heteroscedasticity heading, and then select the Analyze heteroscedasticity check box. Assign the price variable to the Variables on the variance function role.
To run the task, click .

Here is a subset of the results:

To perform a censored regression analysis, you must assign an input data set. To filter the input data source, click Filter Icon

.

You also must assign a variable to the Dependent variable role.

Role	Description
Roles
Dependent variable	specifies the numeric variable for the analysis.
Continuous variables	specifies the independent covariates (regressors) for the regression model. If you do not specify a continuous variable, the task fits a model that contains only an intercept.
Categorical variables	specifies the classification variables. The task generates dummy variables for each level of the categorical variable.
Additional Roles
Group analysis by	enables you to obtain separate analyses of observations for each unique group.

To create a censored regression model:

From the Model type drop-down list, select Censored regression.
Create the model effects.
You can display the main effects model or create a custom model. To create a custom model, select the Custom Model option, and then click Edit. The Model Effects Builder opens. All continuous variables and categorical variables are listed in the Variables pane.
- To create a main effect, select the variable in the Variables pane, and then click Add.
- To create a crossed effect, select the variables in the Variables pane. (You can use Ctrl to select multiple variables.) Then click Cross.
When you finish, click OK. The effects that you specified now appear on the Model tab.

Here is an example of model effects on the Model tab.
Set the upper and lower bounds of the censored variables.

Note: If you do not specify an upper bound or a lower bound, the result is a linear regression model.

Option Name	Description
Methods
Covariance matrix estimator	specifies the method to calculate the covariance matrix of parameter estimates. You can use the default value, or you can choose from these covariance types: Inverse Hessian matrix – the covariance from the inverse Hessian matrix. Outer product matrix – the covariance from the outer product matrix. Outer product and Hessian matrices – the covariance from the outer product and Hessian matrices (the quasi-maximum likelihood estimates).
Optimization
Method	specifies the optimization method to use.
Maximum number of iterations	specifies the maximum number of iterations in the optimization process. You can use the default value, or you can specify a custom value.
Statistics
Select the statistics to display in the results. Here are the additional statistics that you can include in the results: correlations of the parameter estimates covariances of the parameter estimates iteration history of the objective function and parameter estimates

You can create these output data sets:

an output data set that contains the default statistics from the analysis and additional statistics, such as predicted values, residuals, error standard deviation, linear predictor, and so on.
a parameter estimates data set