# Conditional variance in OLS regression

Consider the linear regression model: $$y_{it}=x_{it}\beta+\epsilon_{it}$$ where $x$ is single regressor. The conditional mean of any specific observation is:$$E[y_{it}|x_{it}]=x_{it}\beta$$ under the conditional mean independence assumption. The conditional variance, howevever, is where I am confused. On one had, we can take the variance of both sides of the original equation and get:$$var(y_{it})=\beta^{2}var(x_{it})+var(\epsilon_{it})+2cov(x_{it}\beta,\epsilon_{it})=\beta^{2}\sigma_{x}^{2}+\sigma_{\epsilon}^{2}+0$$

On the other hand, think of the following formula for the variance:$$E\left[y_{it}-E\{y_{it}\}\right]^2 =E[\epsilon_{it}^{2}]=\sigma_{\epsilon}^{2}$$

Which one is correct?

The title of your question "Conditional variance in OLS regression" gives a clue. The first expression

$$\text{Var}(y_{it})=\beta^{2}\sigma_{x}^{2}+\sigma_{\epsilon}^{2}$$

gives the unconditional variance ($x$ is not "conditioned away" and remains in the expression) while the second one

$$\text{E}\left[y_{it}-\text{E}\{y_{it}\}\right]^2 =\sigma_{\epsilon}^{2}$$

gives the conditional variance (conditional on $x$).

But your final equation cannot be right if you are not treating the predictors as fixed. For

$$y_i = \beta x_i + \epsilon_i$$

recalling that

$$E\left(Y \right) = E\left[ E \left(Y|X \right) \right]$$

we have

$$E(y_i) =E\left( E\left(\beta x_i + \epsilon_i|x_i \right) \right) = \beta \mu_x$$

and so

\begin{align} Var(y_i) = E\left(y_i ^2\right) - \left(E(y_i) \right)^2 &= E \left(\beta x_i + \epsilon_i \right)^2 - \beta^2 \mu_x^2 \\ &= \beta^2 E\left(x_i^2 \right) + E(\epsilon^2_i) + 2 E\left( \beta x_i \epsilon_i \right) - \beta^2 \mu_x^2 \\ & = \beta^2 \left(\sigma_x^2 + \mu^2_x \right) + \sigma^2 -\beta^2 \mu_x^2 \\ & = \beta^2 \sigma^2_x + \sigma^2 \end{align}

assuming that the $x_i$ are iid. This is similar to what you obtained in your third equation of course.

• Meaning also that if you ARE treating your predictors as fixed (which is easier and common in introductory classes), both equations are correct, being equal as sigma-x=0. – one_observation Jan 13 '16 at 3:56
• @Sophologist Yes, did I contradict that in any way? – JohnK Jan 13 '16 at 9:57
• Not at all - I just wanted to be explicit about the multiple consequences of that assumption, given that the confusion appears to stem from not having considered it in the first place. – one_observation Jan 13 '16 at 12:12