The QLIM (qualitative and limited dependent variable model) procedure analyzes univariate and multivariate limited dependent variable models in which dependent variables take discrete values or dependent variables are observed only in a limited range of values.

The following are highlights of the QLIM procedure's capabilities:

- fits the following types of models:
- linear regression model with heteroscedasticity
- Box-Cox regression with heteroscedasticity
- probit with heteroscedasticity
- logit with heteroscedasticity
- tobit (censored and truncated) with heteroscedasticity
- bivariate probit
- bivariate tobit
- sample selection and switching regression models
- multivariate limited dependent variables
- stochastic frontier production and cost models

- multivariate models can contain discrete choice and limited endogenous variables in addition to continuous endogenous variables
- supports bounds and restrictions on parameters
- provides Wald, Lagrange multiplier, and likelihood ratio tests
- provides options to control the nonlinear optimization
- supports classification (CLASS) variables
- obtain separate analyses on observations in groups
- performs weighted estimation
- enables you to output data and estimates that can be used in other analyses

For further details, see the *SAS/ETS ^{®} User's Guide*

- Example 27.1: Ordered Data Modeling
- Example 27.2: Tobit Analysis
- Example 27.3: Bivariate Probit Analysis
- Example 27.4: Sample Selection Model
- Example 27.5: Sample Selection Model with Truncation and Censoring
- Example 27.6: Types of Tobit Models
- Example 27.7: Stochastic Frontier Models
- Example 27.8: Bayesian Modeling