# Zero conditional mean assumption (how can in not hold?)

Zero conditional mean of the error term is one of the key conditions for the regression coefficients to be unbiased.

My question is: how can this assumption at all be violated if errors are equal to real observations of Y minus their conditional means (means for a slice of the sample described by the same value of X)?

Shouldn't the conditional expected value (for a slice of the sample described by the same value of X) of such errors always be equal to zero?

• It may help to distinguish between error $\epsilon$, and residual. Apr 29, 2016 at 21:47
• Nov 28 at 13:37

In a more technical parlance, I believe your asking, is the strict exogeneity assumption ever violated. Where the strict exogeneity assumption is...

$$E(\epsilon|X)=0$$

In practice this happens all the time. As a matter of fact the majority of the field of econometrics is focused on the failure of this assumption. When does this happen...

Let's assume that $$\epsilon \sim N(0,1)$$, so $$E(\epsilon) = 0$$. We know that if $$\epsilon$$ and $$X$$ are independent then $$E(\epsilon|X) = E(\epsilon) = 0$$. However, what if $$X$$ and $$\epsilon$$ are correlated such that $$Cov(X,\epsilon) = E(X'\epsilon) - E(X)E(\epsilon) = E(X'\epsilon) \neq E(\epsilon) = 0$$. This implies that $$E(\epsilon|X) \neq 0$$

Clearly the strict exogeneity assumption fails if $$X$$ and $$\epsilon$$ are correlated. The question is, does this ever happen? The answer is yes. As a matter of fact, outside of experimental settings, it happens more often then not. The most common example is omitted variable bias. Matthew Gunn's post discusses this. Another pedagogical example is as follows, imagine you run a regression of ice cream sales over time on the number of people wearing shorts over time. You will likely get a very large and significant parameter estimate. However you're not going to go running to Haagen Daz executives telling them they should start running advertisements for summer wear. It is obvious that there is a missing variable, temperature. This is a violation of the strict exogeneity assumption because number of people wearing shorts ($$X$$) is correlated with our omitted variable temperature which is contained in the error term ($$\epsilon$$)

Notice that the parameter estimate in our simple ice cream sales on number of shorts model is biased. Once we include the temperature in the model the, the number of shorts parameter will change. More formally:

$$\hat \beta = (X'X)^{-1}X'Y = \beta + (X'X)^{-1}X'\epsilon$$ If $$X$$ and $$\epsilon$$ are correlated then ...

$$E(\hat \beta) = \beta + (X'X)^{-1}E(X' \epsilon)$$

So the bias is $$(X'X)^{-1}E(X' \epsilon)$$ which vanishes if the $$E(X' \epsilon)=0$$

• Let's look at it geometrically. The population regression line goes through the averages of all Y values, each of which corresponds to a single X value. The average conditional errors are simply the sums of differences between each actual Y value corresponding to a single X value and the average of Y for this X, all of this divided by a number of errors. This number should always be zero. Apr 29, 2016 at 22:32
• As conjuteprior mentions you are confusing errors (i.e. from the true population DGP) and residuals (the "errors" you get when you estimate your model). By construction there will be no correlation between you residuals and data. That, however, does not preclude your data from being correlated the true unobserved errors. This is what makes the violations of the strict exogeneity assumption so vexing. Namely, your model will not be able to tell you if your violating it. See the update above. Apr 30, 2016 at 0:21

In American football, the total score is given by:

Total football score = 6 * (Touchdowns) + 1 * (ExtraPoints) + 2 * (TwoPointConversions) + 2 * (safeties) + 3 * field goals.

But if you ran the regression:

TotalFootBallScore = b1 * touchdowns + b2 * fieldgoals + e

You wouldn't estimate a value of 6 for b1. Regress the total football score on number of touchdowns and field goals, and you would almost certainly estimate that touchdowns are worth more 7 or more points rather than 6. Your error term e in this case contains the points scored from extra points and two points conversions, and those are almost certainly not zero conditional on knowing the number of touchdowns.