# AR1 and Law Of Iterated Expectations : No serial correlation

In the AR(1) model $y_{t}=\beta_{0}+\beta_{1}y_{t-1}+u_{t}$, assuming $E(u_{t-1}|y_{t-1},y_{t-2}...)=0$, how does the law of iterated expectations ensure that the errors must be uncorrelated: $E(u_{t}u_{s}|x_{t},x_{s})=0$?

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Is this a homework problem? In any event, the statement that errors are uncorrelated is an assumption of the AR(1) model, not a (genuine) derivable result. –  Arthur Small Jan 14 '13 at 20:36
No not homework - reading through Woolridge intro - page 385. It is stated that given AR(1) and the assumption that the error term has zero mean given all past values of y, the errors are uncorrelated. –  B_Miner Jan 14 '13 at 20:42

Consider $s \neq t$. The law of iterated expectations tells us that

$$\begin{equation*} \mathbb{E}[u_su_t \mid y_{s-1}, y_{t-1}] = \mathbb{E}_{u_s}\left[\mathbb{E}[u_su_t \mid u_s, y_{s-1}, y_{t-1}]\right] = \mathbb{E}_{u_s}\left[u_s\mathbb{E}[u_t \mid u_s, y_{s-1}, y_{t-1}]\right]. \end{equation*}$$

Suppose that $s < t$, with the opposite case following by symmetry.

The AR(1) process has the property that, for $y_t = \beta_0 + \beta_1 y_{t-1} + u_t$, $\mathbb{E}[u_t \mid y_{t-1}, y_{t-2}, \dots] = \mathbb{E}[u_t \mid y_{t-1}] = 0$. In words, if I know what happened last period, then nothing else in the past can help me guess what happens today.

Rewriting our AR(1) equation reveals that $u_s = y_s - \beta_0 - \beta_1 y_{s-1}$; there is a one-to-one transformation betweent $u_s$ and $y_s$. As a result, conditioning on $u_s$ is equivalent to conditioning on $y_s$.

Using this relationship and our fact about the AR(1) process, we find that $$\begin{equation*} \mathbb{E}[u_t \mid u_s, y_{s-1}, y_{t-1}] = \mathbb{E}[u_t \mid y_s, y_{s-1}, y_{t-1}] = \mathbb{E}[u_t \mid y_{t-1}] = 0. \end{equation*}$$

Plugging this in to the LIE result above gives that $$\begin{equation*} \mathbb{E}_{u_s}\left[u_s\mathbb{E}[u_t \mid u_s, y_{s-1}, y_{t-1}]\right] = \mathbb{E}_{u_s}\left[u_s \times 0\right] = 0. \end{equation*}$$

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In the first part, the conditioning is on $y_{s-1},y_{t-1}$ correct? And when you condition on on $u_{s}$ that amounts to making $u_{s}$ a constant? –  B_Miner Jan 25 '13 at 0:47
Yes to both points. –  Charlie Jan 25 '13 at 15:44