What is a fixed effect in a mixed model compared to a fixed effect model for panel data? I am confused about the expression "fixed effect" in the context of mixed models. I am more familiar with the terms like "fixed effects" and "random effects" in context of econometrics and the analysis of panel data.
The understanding of "random effects" seems similar in both disciplines and the "random effects model" in econometrics is equivalent to a "mixed model with random intercept". See for example: How exactly does a "random effects model" in econometrics relate to mixed models outside of econometrics?
But what is about the "fixed effect". In econometrics, with the help of fixed effects e.g. all time invariant effects will be absorbed. Or in other words, a dummy variable for each individual is introduced.
But, what is "fixed effect" in context of mixed models? In simple words?
Hereby, I mean more the intuition and not the mathematically way.
It is not the same as a fixed effect in econometrics. That is what I understand, but I have no clue what it is instead.
EDIT:
Meanwhile I found this explanation in context of mixed models:
"The fixed effects are analogous to standard regression
coefficients and are estimated directly."
This means to me, that a fixed effect in mixed models is not the same as fixed effects in econometrics. Or in other words: Fixed effects are the variables that are not declared as random effects (--> standard regression
coefficients as in linear regression).
Source (slide 2): http://fmwww.bc.edu/EC-C/S2013/823/EC823.S2013.nn07.slides.pdf
 A: Even within the disciplines, there is sadly no council that ensures consistent usage. However, in my experience the key distinction as that in early econometric panel models they tended to be referring to the intercept, whereas in other fields they are referring to anything. 
Consider a model predicting how much a person weighs, where you have 10 observations taken over 10 years for 1,000 children. In econometrics the 'fixed effects' model is one where you have a separate intercept for each of the 1,000 children (such models are not typically estimated with dummy variables, but it is OK to think of them that way). The random effects model is one where you instead assume that the intercept is a random variable and estimate its mean and variance. 
Now, let's add into the model a predictor which is the height of the child. If we estimate a single coefficient, we treat the effect as being fixed. If we instead treat it as a random variable, we estimate the average effect and the variance of the effect (if assuming normally distributed random variables).
If you have some predictors that are assumed to be random variables and others that are not, be they the intercept or anything else, you have a mixed model.
