The PANEL procedure analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. This type of pooled data on time series cross-sectional bases is often referred to as panel data.

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

- fits the following types of models:
- one-way and two-way models
- fixed-effects and random-effects models
- autoregressive models
- moving average models

- estimates the regression parameters under three common error structures:
- Fuller and Battese method (variance component model)
- Parks method (autoregressive model)
- Da Silva method (mixed variance component moving average model)

- supports fixed-effects and random-effects models, which handle both balanced and unbalanced data
- provides Wald, Lagrange multiplier, and likelihood ratio tests
- provides
*F*specification test for fixed-effects models - provides Hausman specification test for random-effects models
- provides between estimators, pooled estimators, and dynamic panel estimators that use the generalized method of moments
- supports restrictions on parameters
- produces heteroscedasticity-consistent covariance matrices
- provides a LAG statement that makes the creation of lagged values easy
- supports classification (CLASS) variables
- provides a variety of estimates and statistics, including the following:
- underlying error components estimates
- regression parameter estimates
- standard errors of estimates
*t*tests- correlation matrix of estimates
- covariance matrix of estimates
- autoregressive parameter estimate
- cross-sectional components estimates
- autocovariance estimates

- provides a FLATDATA statement that enables the data to be in compressed form
- obtains separate analyses on observations in groups
- supports ODS Graphics
- 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 26.1: Analyzing Demand for Liquid Assets
- Example 26.2: The Airline Cost Data: Fixtwo Model
- Example 26.3: The Airline Cost Data: Further Analysis
- Example 26.4: The Airline Cost Data: Random-Effects Models
- Example 26.5: Panel Study of Income Dynamics (PSID): Hausman-Taylor Models
- Example 26.6: Dynamic Panel Estimation of Cigarette Sales Data
- Example 26.7: Using the FLATDATA Statement