<p>It is not necessarily true that lagged $y$ are endogenous. That depends entirely on the structure you assume for $u_t$. If $u_t$ is white noise, there is no endogeneity, in the sense that </p>

<p>$$ E(u_t|\mathbf{y}) = 0 $$</p>

<p>where $\mathbf{y}=\{y_{t-1}, \cdots , y_{t-p}\}$.</p>

<p>This is trivial to prove, once you realise that the above equality can also be expressed as </p>

<p>$$E(u_t y_{t-i})=0 \,\,\quad, i=\{1, \cdots, p\} $$</p>

<p>using the <a href="https://en.wikipedia.org/wiki/Law_of_total_expectation" rel="nofollow noreferrer">Law of Total Expectations</a>.</p>

<p>Conversely, it is trivial to show that if $u_t$ follows an autoregressive process, the above conditions does not hold anymore, in which case there is endogeneity, and OLS estimates are inconsistent.</p>

CC BY-SA 3.0
2017-07-06T16:31:56.770Z
That takes care of endogeneity with the error term and $y_t$, but wouldn't there be multicolinearity among the independent variables?
EconJohn
CC BY-SA 3.0
2017-07-06T16:40:29.960Z
@EconJohn There is, but that does not invalidate the endogeneity condition, which is what makes OLS inconsistent (and what you were asking for, as far as I understood). Multicolinearity by itself is not a problem (unless there is perfect colinearity, which seems weird in TS, unless the series is a constant, and so unfit for the model). Its actually part of the reason why TS usually gives so high $R^2$s (since dependent and independent are likely to be highly correlated), compared with cross-section.
luchonacho
CC BY-SA 3.0
2017-07-06T18:18:19.610Z
@EconJohn The existence of multicolinearity is the very reason why we use multiple regression (many explanatory variables) and not just simple regression (one explanatory variable). If you meant to refer to "severe-perfect" multicolinearity that creates _technical_ issues as regards the execution of the LS estimator, this is a per-case issue.
Alecos Papadopoulos
CC BY-SA 3.0
2017-07-06T16:07:29.833Z
2017-07-06T16:13:13.510Z
2017-07-06T16:13:13.510Z
luchonacho
<p>an AR(p) process is given by:</p>

<p>$$y_t=\phi_0+\phi_1y_{t-1}+\phi_2y_{t-2}+...+\phi_{t-p}y_{t-p}+u_t$$</p>

<p>In such a model we have endogenous variables. My question is, why is this not an issue when dealing with time series data.</p>

<econometrics>
Endogeneity and the AR model?
EconJohn
# Endogeneity and the AR model?

(Asked by: username_0 on 2017-07-06)

an AR(p) process is given by:

$$y\_t=\phi\_0+\phi\_1y\_{t-1}+\phi\_2y\_{t-2}+...+\phi\_{t-p}y\_{t-p}+u\_t$$

In such a model we have endogenous variables. My question is, why is this not an issue when dealing with time series data.

**Tags:** econometrics


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## 1 answers

### Answer by username_1 on 2017-07-06. Score: 2 | Accepted answer

It is not necessarily true that lagged $y$ are endogenous. That depends entirely on the structure you assume for $u\_t$. If $u\_t$ is white noise, there is no endogeneity, in the sense that

$$ E(u\_t|\mathbf{y}) = 0 $$

where $\mathbf{y}=\{y\_{t-1}, \cdots , y\_{t-p}\}$.

This is trivial to prove, once you realise that the above equality can also be expressed as

$$E(u\_t y\_{t-i})=0 \,\,\quad, i=\{1, \cdots, p\} $$

using the [Law of Total Expectations](https://en.wikipedia.org/wiki/Law_of_total_expectation).

Conversely, it is trivial to show that if $u\_t$ follows an autoregressive process, the above conditions does not hold anymore, in which case there is endogeneity, and OLS estimates are inconsistent.

> **Comments:**
> **username_0**: That takes care of endogeneity with the error term and $y_t$, but wouldn't there be multicolinearity among the independent variables?  
> **username_1**: @username_0 There is, but that does not invalidate the endogeneity condition, which is what makes OLS inconsistent (and what you were asking for, as far as I understood). Multicolinearity by itself is not a problem (unless there is perfect colinearity, which seems weird in TS, unless the series is a constant, and so unfit for the model). Its actually part of the reason why TS usually gives so high $R^2$s (since dependent and independent are likely to be highly correlated), compared with cross-section.  
> **username_2**: @username_0 The existence of multicolinearity is the very reason why we use multiple regression (many explanatory variables) and not just simple regression (one explanatory variable). If you meant to refer to "severe-perfect" multicolinearity that creates _technical_ issues as regards the execution of the LS estimator, this is a per-case issue.  

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