# Questions tagged [fixed-effects-model]

In biostatistics, fixed-effects may mean population-average effects. In econometrics, fixed-effects may represent the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random.

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### What is the difference between fixed effect, random effect and mixed effect models?

In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect and mixed effect models?
3answers
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### R's lmer cheat sheet

There's a lot of discussion going on on this forum about the proper way to specify various hierarchical models using lmer. I thought it would be great to have all ...
2answers
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### What is a difference between random effects-, fixed effects- and marginal model?

I am trying to expand my knowledge of statistics. I come from a physical sciences background with a "recipe based" approach to statistical testing, where we say is it continuous, is it normally ...
4answers
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### Standard error clustering in R (either manually or in plm)

I am trying to understand standard error "clustering" and how to execute in R (it is trivial in Stata). In R I have been unsuccessful using either plm or writing my ...
3answers
50k views

### When to use fixed effects vs using cluster SEs?

Suppose you have a single cross-section of data where individuals are located within groups (e.g. students within schools) and you wish to estimate a model of the form ...
6answers
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### What is the mathematical difference between random- and fixed-effects?

I have found a lot on the internet regarding the interpretation of random- and fixed-effects. However I could not get a source pinning down the following: What is the mathematical difference between ...
2answers
37k views

### REML or ML to compare two mixed effects models with differing fixed effects, but with the same random effect?

Background: Note: My data set and R code are included below text I wish to use AIC to compare two mixed effects models generated using the lme4 package in R. Each ...
5answers
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### What is the upside of treating a factor as random in a mixed model?

I have a problem embracing the benefits of labeling a model factor as random for a few reasons. To me it appears like in almost all cases the optimal solution is to treat all of the factors as fixed. ...
1answer
17k views

### Incidental parameter problem

I always struggle to get the true essence of the incidental parameter problem. I read in several occasions that the fixed effects estimators of nonlinear panel data models can be severely biased ...
2answers
3k views

### Big disagreement in the slope estimate when groups are treated as random vs. fixed in a mixed model

I understand that we use random effects (or mixed effects) models when we believe that some model parameter(s) vary randomly across some grouping factor. I have a desire to fit a model where the ...
4answers
12k views

### Fixed effect vs random effect when all possibilities are included in a mixed effects model

In a mixed effects model the recommendation is to use a fixed effect to estimate a parameter if all possible levels are included (e.g., both males and females). It is further recommended to use a ...
1answer
11k views

### When is it necessary to include the lag of the dependent variable in a regression model and which lag?

The data we want to use as dependent variable looks like this (it is count data). We fear that since it has a cyclic component and trend structure the regression turns out to be biased somehow. We ...
4answers
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1answer
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### Three way fixed effects vs combining two of the effects

I have panel data on employment that varies by year, sector and location and thus would like to run a fixed effects regression considering these 3 dimensions. The issue is that I use R and the ...
1answer
6k views

### Model selection in mixed-model context using lmer

Assume I have two factors A and B potentially predicting my outcome Y. Now I would like to test for fixed-effects using likelihood ratio test to find the best model. ...
2answers
3k views

### Zero-inflated two-part models for semi-continuous data

I am trying to study predictors of companies' pollution output of some specific chemicals. The data I am using have many 0's (i.e., the company did not pollute at all with those chemicals) and then ...
1answer
1k views

### Real motivation for using mixed effect models, and when to use them and when not to

My question might sound naïve, but despite my internet search, I wasn't able to find a satisfactory answer. I've been introduced to linear regression, linear fixed effect and linear mixed effect ...
1answer
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### Dealing with hierarchical (panel, multi-level) data and fixed effects in LASSO?

The question pretty much explains itself. When running a Lasso regression on a lot of indexed (say by time and location) explanatory variables, is it best practice to transform all data using a ...
1answer
5k views

### Difference-in-difference in panel data

Under which conditions should we expect the difference-in-difference estimate to be equal to the equivalent panel data model? Strictly speaking, whenever we have a experiment that offers a well ...
2answers
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### Order of variables in R lm model

Is the order of variables in an R model supposed to be significant? For some reason, the two models below result in different coefficients associated with fm and yr (which are supposed to model fixed ...
1answer
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### Difference between fixed effects models in R (plm) and Stata (xtreg)

I'm trying to re-create an analysis done using Stata function xtreg (though I don't have the code) with ...
1answer
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### Are the relations in fixed, random and mixed effect models and multilevel models causal?

In fixed, random and mixed effect models, and multilevel models, the response random variable is represented as a function of some explanatory variables and random errors. I was wondering if the ...
1answer
6k views

### cluster-robust standard errors are smaller than unclustered ones in fgls with cluster fixed effects

I'm currently working on some experimental data. The experimental design consists of two treatments. In each treatment, 20 subjects are randomly matched in pairs and participate to a simple game. The ...