Panel Data: Count Data Regression Task :: SAS(R) Studio 3.3: User's Guide

About the Panel Data: Count Data Regression Task

The Panel Data: Count Data Regression task performs count data regression of a continuous dependent variable. This variable is a nonnegative integer value from Poisson or negative binomial distributed panel data.

Note: This task is available only if you are running the first maintenance release for SAS 9.4 (or later) and SAS/ETS 13.1 (or later).

Example: Count Data Regression with Panel Data

To create this example:

Create the WORK.LONG97DATA data set. For more information, see LONG97DATA Data Set.
In the Tasks section, expand the Econometrics folder and double-click Panel Data: Count Data Regression. The user interface for the Panel Data: Count Data Regression task opens.
On the Data tab, select the WORK.LONG97DATA data set.

Assign columns to these roles:

Role	Column Name
Dependent variable	art
Continuous variables	ment phd mar
Categorical variables	kid5
Cross-sectional ID	fem

To run the task, click .

Here is a subset of the results:

Results from the Panel Data: Count Data Regression Task

Assigning Data to Roles

To run the Count Panel Data Regression task, you must assign columns to the Dependent variable and Cross-sectional ID roles.

Role	Description
Dependent variable	specifies the numeric column that has nonnegative integer or count values. The Distribution option specifies the type of model to be analyzed. You can specify these types of models: Poisson regression model negative binomial regression model with a linear variance function negative binomial regression model with a quadratic variance function
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 variables to use to group data in the analysis.
Cross-sectional ID	specifies the cross-section for each observation. You can specify whether the error component model is fixed or random.

Setting Options

Option	Description
Methods
Type of covariances of the parameter estimates	specifies the type of covariance matrix of the parameter estimates. You can specify these types of matrices: the covariance from the inverse Hessian matrix the covariance from the outer product matrix the covariance from the outer product and Hessian matrices (also called the quasi-maximum-likelihood-estimates)
Include the intercept in the model	specifies whether to include the intercept in the model.
Optimization
Method	specifies the iterative minimization method to use.
Maximum number of iterations	specifies the maximum number of iterations for the selected method.
Statistics
You can specify whether to include the statistics that the task creates by default and any additional output tables in the results. Here are the additional statistics that you can include in the results: correlation matrix of the parameter estimates covariance matrix of the parameter estimates iteration history of the objective function and parameter estimates