[R-sig-ME] Relationship between mixed-effects models and fixed-effects models

[James Pustejovsky]

Here's a good primer:

McNeish, D., & Kelley, K. (2019). Fixed effects models versus mixed effects
models for clustered data: Reviewing the approaches, disentangling the
differences, and making recommendations. *Psychological Methods*, *24*(1),
20.
https://www3.nd.edu/~kkelley/publications/articles/McNeish_Kelley_PsychMethods_2019.pdf

The challenge in these discussions is that econometricians use fixed
effects semi-parametrically, by specifying a *minimal* set of assumptions
regarding the conditional mean of the response (given the observed
predictors and the cluster-specific intercepts) and dependence structure.
Thus, many of the discussions will avoid writing down full probability
distributions. Another challenge is that econometricians tend to be worried
about confounding and dependence between the distribution of the predictors
and the distribution of the cluster-specific intercepts.

On Mon, Jun 7, 2021 at 10:14 AM Douglas Bates <dmbates using gmail.com> wrote:

> Occasionally I encounter discussions of what are called fixed-effects
> models in econometrics but I haven't seen descriptions of the underlying
> statistical model.  Can anyone point me to a description of these models,
> in particular a description in terms of a probability distribution of the
> response? I would be particularly interested in a discussion of how they
> relate to mixed-effects models as we think of them in lme4 and nlme.
>
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