Editing Omitted-variable bias (section)

==Effect in ordinary least squares==
The [[Gauss–Markov theorem]] states that regression models which fulfill the classical linear regression model assumptions provide the [[Efficiency (statistics)|most efficient]], linear and [[Bias of an estimator|unbiased]] estimators. In [[ordinary least squares]], the relevant assumption of the classical linear regression model is that the error term is uncorrelated with the regressors.

The presence of omitted-variable bias violates this particular assumption. The violation causes the OLS estimator to be biased and [[Consistency (statistics)|inconsistent]]. The direction of the bias depends on the estimators as well as the [[covariance]] between the regressors and the omitted variables. A positive covariance of the omitted variable with both a regressor and the dependent variable will lead the OLS estimate of the included regressor's coefficient to be greater than the true value of that coefficient. This effect can be seen by taking the expectation of the parameter, as shown in the previous section.