Fixed Effects Regression Methods for Longitudinal Data Using SAS

6.4. A Compromise between Fixed Effects and Random Effects

In the previous section, we obtained a fixed effects model by starting with a random effects model and then allowing for all possible correlations between the random effect α and the time-varying explanatory variables. But perhaps all those correlations aren't really needed. Output 6.4 shows estimated correlations and covariances between α and the time-varying variables produced by PROC CALIS. It appears that the correlations with the SELF variables are very small and not statistically significant, whereas the correlations with the POV variables are somewhat larger, and two of the three are statistically significant. This suggests that we could set the SELF correlations equal to 0 without ...