# What variation are multiple fixed effects explaining?

I wanted to share a thought experiment and get some reactions. I could not find this discussed in plain language. If I have a panel data set that has four variables a y, x, person1_id, and person2_id and I run the following regression, (y ~ x + person1_id + person2_id) what variation is the coefficient on x explaining?

Imagine the data are the number of smiles (y) and person2's t-shirt color brightness (x). Is this regression explaining the variation in the number of smiles person1 gives person2 based on person2's t-shirt color brightness?

It is clear if we exclude the second set of dummies (person2_id) and have the following regression (y ~ x + person1_id) that the variation that this is explaining is how the same person (person1_id) smiles based on t-shirt color brightness.

• This is actually a really interesting question and I hope I can find the time to write a nice answer... Jun 7, 2019 at 18:44
• the question seems a bit misspecified. Shirt color is a scale variable? if not then it needs to be some sort of dummy (green one all others 0). but lets call it shirt brightness, rather than color (so that it can be a scale). Then the regression basically predicts the size of the smile (?) given person 1 and 2, given an average brightness score. I would say. your variables are not very clearly specified, and I think this is why you are having the problems - number of smiles? like on a scale from 1 to 10? otherwise it doesnt really make sense. Jun 7, 2019 at 19:47
• In the example you gave, it seems that we'd never have person1_id == person2_id in the same row of data, which means that some combinations of (person1_id, person2_id) are not observed. Are you only interested in this kind of "incomplete" design, or are you interested more generally in any design where we look at the effect of some X after adjusting for multiple, additive fixed factors? I suspect it's the latter but can you confirm? Jun 8, 2019 at 17:27
• @JakeWestfall I am generally interested in the latter but the former is making more sense to me right now.
– LF12
Jun 11, 2019 at 1:49

Assumptions:

• Let assume, that observations are independent i.e. $$Cov(\varepsilon_{i,j}, \varepsilon_{j,i}) = 0$$ for $$i \neq j$$ and $$y_{i,i}$$ do not exist.

This is a strong assumption, but situation need somehow to be clarified. Are smiles mutually generated? How they are measured? Is it possible, that $$\varepsilon_{i,j}$$ and $$\varepsilon_{j,i}$$ are correlated? Maybe $$y_{i,j} = y_{j,i}$$? There are many possibilities, so let assume this one.

• Let assume then that precisely $$y_{i,j}$$ is the number of smiles person $$i$$ 'gives' to person $$j$$ based on the of brightness of $$j$$'s shirt ($$x_j$$), the generosity of person $$i$$ of giving smiles away ($$z_i$$), and the $$appeal$$ of person $$j$$ ($$v_j$$). The observation is one-sided smilegiving fact.

• The true, full model is:

$$y_{i,j} = \beta_0 + \beta_1 x_j + \beta_2v_j + \beta_3 z_i + \varepsilon_{i,j}$$

• We can not observe $$v_j$$ and $$z_i$$, but we observe $$x_j$$. What are the consequences? The estimator may be inconsistent and biased. This situation happens only, when the variable $$x_j$$ is correlated with $$v_j$$, $$z_i$$ or both.

• But hey, we have this smilegiving setting! We can think about it theoretically! How is it possible, that the generosity of person $$i$$ is correlated to brightness of person's $$j$$ shirt? Maybe it can somehow happen? How? We need another assumption - let assume that such thing do not happen, i.e. $$x_j$$ and $$z_i$$ are independent, and then uncorrelated.

• We can also think of second variable: is it possible that more applealing people wear brighter t-shirts? Maybe otherwise? Let assume, that there are two possibilities: independence and correlation.

Consequences of omitting of a variable:

• Instead of full model, we estimate model:

$$y_{i,j} = \beta_0 + \beta_1 x_j + \beta_2v_j + \varepsilon_{i,j}$$

There are two consequences of such an action: First, nothing wrong happened to $$\hat{\beta_1}$$ - it is still consistent and unbiased. That is great thing in statistical (or causal) inference. Second, now variable $$z_i$$ is included in $$\varepsilon_{i,j}$$. The precision of prediction is lower, errors are bigger.

• Instead of full model we estimated model:

$$y_{i,j} = \beta_0 + \beta_1 x_j + \varepsilon_{i,j}$$

We assumed two possibilities: that $$x_j$$ and $$v_j$$ are independent, or that they are correlated.

1. If they are independent, then the situation is exactly the same as before: our estimator is unbiased and consistent, while prediction is less precise.

2. What interests us, is other option: when the variables $$x_j$$ and $$v_j$$ are correlated. Now the estimator is biased and inconsistent.

• Statistical (and dreamly causal) inference is dead in option 2, while it was perfectly fine in option 1.

The interpretation of $$\hat{\beta_1}$$ may be: When we observe change of $$x_j$$ by one unit, then we observe change in $$y_{i,j}$$ by $$\hat{\beta_1}$$, assuming other variables included in model do not change.

Some say ceteris paribus, but this seems wrong, because when error term is correlated with our variable $$x_j$$, then in fact 'other things' are not fixed - the error changes systematically with change of the $$x_j$$. Fixed remain only variables included in model.

• We can stil predict. The prediction is worse, than in any model including more variables, but it is still better, than no prediction.

• In second model, we suddenly encountered hidden problem - error clusterisation at the level of $$j$$. It will affect our estimators of $$\beta_1$$. I will not analyse this in sake of any simplicity.

Introduction of fixed effects:

• Now we get it: we observe people many times, both this who give smiles, and those, who get them. We can introduce fixed effects! But what are the consequences?

• At first, lets introduce dummy for $$i$$. This is not a very drastic case:

$$y_{i,j} = \beta_0 + \beta_1 x_j + \varepsilon_{i,j}$$

we estimate model:

$$y_{i,j} = \beta_0 + \gamma_i + \beta_1 x_j + \varepsilon_{i,j}$$

Three things happen:

1. R$$^2$$ increases to the exact of model: $$y_{i,j} = \beta_0 + \beta_1 x_j + \beta_3 z_i+ \varepsilon_{i,j}$$. We explained variance of variable $$z_i$$.
2. We can not predict for an unknown person. If we want so make a prediction we must put there $$i$$. This is the cost of such model and such prediction.
3. Estimator of $$x_j$$ is inconsistent and biased as it was before, because variable $$v_j$$ is still omitted.
• Let then introduce dummy for $$j$$. Instead of model:

$$y_{i,j} = \beta_0 + \gamma_i + \beta_1 x_j + \varepsilon_{i,j}$$

we estimate model:

$$y_{i,j} = \beta_0 + \gamma_i + \theta_j + \beta_1 x_j + \varepsilon_{i,j}$$

Four things happen:

1. R$$^2$$ increases to the level of full model $$y_{i,j} = \beta_0 + \beta_1 x_j + \beta_2v_j + \beta_3 z_i + \varepsilon_{i,j}$$. We explained variance of full model (great!).
2. As before, we can not predict for an unknown person, we must put $$i$$ and $$j$$ there.
3. We get rid of clustered error problem. Error no longer contains $$v_j$$ (see 4.).
4. Estimator of $$\beta_1$$ is not estimated due to full collinearity with dummies. AUCH.

Some playground

Below an R code, which allows to simulate, and believe this story by oneself:

n = 200
d = data.frame(id = 1:(n*n-n))

z = rnorm(n)
v = rnorm(n)
x = v + rnorm(n)

k = 0
for(i in 1:n){
z_i = z[i]
for(j in 1:n){
v_j = v[j]
x_j = x[j]
if(i!=j){
k = k+1
print(k)
y_ij = x_j + v_j + z_i + rnorm(1)
d[k, 'i'] = i
d[k, 'j'] = j
d[k, 'z_i'] = z_i
d[k, 'v_j'] = v_j
d[k, 'x_j'] = x_j
d[k, 'y_ij'] = y_ij
}
}
}

d$$i = as.factor(d$$i)
d$$j = as.factor(d$$j)

m1 = lm(y_ij ~ x_j + v_j + z_i, data = d)
summary(m1)

m2 = lm(y_ij ~ x_j + v_j, data = d)
summary(m2)

m3 = lm(y_ij ~ x_j, data = d)
summary(m3)

m4 = lm(y_ij ~ i  + x_j, data = d)
summary(m4)

m5 = lm(y_ij ~ z_i  + x_j, data = d)
summary(m5)

m6 = lm(y_ij ~ j  + i + x_j, data = d)
summary(m6)

• This does not satisfy the plain language so it would be great if you could add a few sentences that sums up what variation each model is explaining in plain language
– LF12
Jun 19, 2019 at 14:48
• Sorry to hear that. I did everything i could to make it plain, however I could not make it shorter, as the question is not one-dimensional. If you want a shortcut, just read third bullet under Introduction of fixed effects:. Tere are both statements about variance, that this model explains and the $x_j$ coefficient.
– cure
Jun 20, 2019 at 9:38