Zero Conditional Mean (ZCM), or Strict Exogeneity, is given by:
$E[u_t|X]=0, t=1,...,T$

Is it true that this implies:

  • Zero Unconditional Mean: $E[u_t]=0, \forall t$
  • Contemporaneous Exogeneity: $E[u_t|x_t]=0, \forall t $ (Where $x_t$ is a vector of explanatory variables)

  • $E[x_su_t]=0, \forall t,s$

And are there any other things that ZCM imply that are particularly useful?


Well consider the Law of Iterated expectation $$\mathbb E[ \mathbb E[Y\lvert W]]= \mathbb E[Y]$$

and apply it with $Y=\mathbf u$ and $W = \mathbf X$, to get $\mathbb E[ \mathbb E[\mathbf u \lvert \mathbf X]]= \mathbb E[\mathbf u]$ and $\mathbb E[ \mathbb E[\mathbf u \lvert \mathbf X]] = \mathbb E[0] = 0$, hence $\mathbb E[\mathbf u]=0$.

Consider a generalized version of Law of Iterated Expectation mentioned in the post A generalization of the Law of Iterated Expectations which states that

$$\mathbb E[\mathbb E[Y\lvert W_1,W_2] \lvert W_2] = \mathbb E[Y\lvert W_2]$$

and let $W_1 = \mathbf X\setminus \mathbf x_t$ (read $\mathbf X$ except the vector $\mathbf x_t$) and let $W_2=\mathbf x_t$ then

$$\mathbb E[\mathbb E[u_t\lvert \mathbf X]\lvert \mathbf x_t] = \mathbb E[\mathbb E[u_t\lvert \mathbf X\setminus \mathbf x_t,\mathbf x_t]\lvert \mathbf x_t] = \mathbb E[u_t \lvert \mathbf x_t]$$ and as before it must be 0 because $\mathbb E[u_t\lvert \mathbf X]=0$.

Use the same argument to get $\mathbb E[u_t \lvert \mathbf x_s] = 0$ for all $s,t = 1,...,T$ and use this to conclude that

$$\mathbb E[u_t \mathbf x_s] = \mathbb E[\mathbb E[u_t \mathbf x_s\lvert \mathbf x_s] ] = \mathbb E[ \mathbf x_s \mathbb E[u_t \lvert \mathbf x_s] ] = \mathbb E[ \mathbf x_s \cdot 0 ] = 0$$

So the answer must be yes they are all valid statements and further more $\mathbb E[u_t \lvert \mathbf x_s] = 0$ for all $s,t = 1,...,T$ as was used.

An intuitive statement of the statement $$\mathbb E[\mathbb E[Y\lvert W_1,W_2] \lvert W_2] = \mathbb E[Y\lvert W_2]$$ is that the least information set always dominates so you also have $$\mathbb E[\mathbb E[Y\lvert W_2] \lvert W_1, W_2] = \mathbb E[Y\lvert W_2]$$ again this is mentioned in the post referred to above.

  • $\begingroup$ I think the variables in the first 3 lines are mixed up, but I get the point, thanks. $\endgroup$ – DQd Dec 28 '18 at 17:50
  • $\begingroup$ pls accept the answer if it was useful. I have edited the mix up. $\endgroup$ – Jesper for President Dec 28 '18 at 18:13
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    $\begingroup$ Shouldn't the first equation be $\mathbb E[\mathbb E[Y\mid W]]=\mathbb E[Y]$? $\endgroup$ – Cm7F7Bb Feb 21 '19 at 9:11
  • $\begingroup$ yes offcourse thx $\endgroup$ – Jesper for President Feb 22 '19 at 0:45

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