Random Effects Models and the Analysis of Panel Data

Introduction

Random Effects Models and the Analysis of Panel Data

SAS/STAT software contains two procedures for fitting general linear models to panel data. The GLM procedure fits general linear models involving fixed effects. The more general MIXED procedure fits mixed linear models containing both fixed and random effects.

The MIXED procedure provides easy accessibility to a variety of mixed models useful in many common statistical analyses, including split-plot designs, repeated measures, random coefficients, best linear unbiased predictions, shrinkage estimators, and heterogeneous variances. In the style of the GLM procedure, PROC MIXED fits the specified mixed linear model and produces appropriate statistics.

Some features of PROC MIXED are

covariance structures, including simple random effects, compound symmetry, unstructured, AR(1), Toeplitz, and spatial
syntax similar to that of PROC GLM, using the MODEL, RANDOM, and REPEATED statements for model specification, and the CONTRAST, ESTIMATE, and LSMEANS statements for inferences
appropriate standard errors for all specified estimable linear combinations of fixed and random effects, and corresponding t- and F-tests
subject and group effects that enable blocking and heterogeneity
REML and ML estimation methods
capacity to handle unbalanced data
ability to create a SAS data set corresponding to any printed table

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