# What extra properties does assuming errors are iid have compared to assuming errors are uncorrelated and common variance

In linear regression model, the means of the errors are assumed to be zero. Furthermore, we can assume either that the errors are uncorrelated and have the same variance, or even that the errors are iid. Note normality isn't assumed on the errors.

What extra properties does assuming errors are iid bring to the OLS estimates, compared to assuming errors are uncorrelated and common variance? Thanks!

• Are you treating the regressors as stochastic or as deterministic? Aug 2, 2013 at 21:54
– Tim
Aug 2, 2013 at 23:22

Regarding the asymptotic properties of OLS (consistency and asymptotic normality), note that when the regressors $\mathbf X$ are deterministic, the regressors-error sequence $\left(\mathbf X, \varepsilon\right)$ is in all cases a heterogeneous sequence -because the variability of $\mathbf X$ cannot now be treated as the natural variability of a sequence of identical random variables (since $\mathbf X$ does not contain random variables). And this holds irrespective of whether the errors are identically distributed or just white noise. So the "identically distributed errors" assumption does not bring anything new to the asymptotic properties of OLS, nor does it help make these properties more easy to obtain (compared to the "white noise" assumption). The "independent errors" assumption is rather more influential. Without it: for OLS to be consistent and asymptotically normal, we must assume in addition stronger regularity conditions on the higher moments of the distribution, and in all cases, we cannot let the dependence assumption rage unchecked: $\left(\mathbf X, \varepsilon\right)$ must be a "mixing process" (avoiding technicalities, a mixing process consists of dependent variables, but "rather mildly" dependent, which are eventually "asymptotically independent"). Otherwise the asymptotic OLS properties do not survive. So by assuming independent errors, we essentially avoid stating rather technically advanced requirements for the same OLS asymptotic properties to hold.