Is there such a thing as a random effect?
The estimate, ID ‘s variance = 0, indicates that the level of between-group variability is not sufficient to warrant incorporating random effects in the model; ie. your model is degenerate. As you correctly identify yourself: most probably, yes; ID as a random effect is unnecessary. Few things spring to mind to test this assumption:
How are random effects models used in econometrics?
In econometrics, random effects models are used in panel analysis of hierarchical or panel data when one assumes no fixed effects (it allows for individual effects). A random effects model is a special case of a mixed model.
How are random effect models used in longitudinal data?
Random effect models assist in controlling for unobserved heterogeneity when the heterogeneity is constant over time and not correlated with independent variables. This constant can be removed from longitudinal data through differencing, since taking a first difference will remove any time invariant components of the model.
Can a constant be removed from a random effect model?
This constant can be removed from longitudinal data through differencing, since taking a first difference will remove any time invariant components of the model. Two common assumptions can be made about the individual specific effect: the random effects assumption and the fixed effects assumption.
How is time itself not a random factor?
Time itself is NOT a random factor. County is. So again, when you specify it in the software, County is specified as the subject and Time is the only “variable” you’re putting in as a random effect. It makes it look like Time is a random factor, but it’s not.
What are the random factors in a model?
In the random part of the model, there is one random factor, two random effects, and the residual. I suspect you’re familiar with residuals from linear models. Let’s focus instead on the two random terms.
Is it correct to call a county a random effect?
Calling County or Time a random effect is not just technically incorrect, but it makes it much harder to conceptualize what each of the real random effects is actually measuring. One of the hardest parts of mixed models is understanding which factors to make fixed and which to make random. Learn the important criteria to help you decide.