What assumption is wrong about OLS?

The Assumption of Linearity (OLS Assumption 1) – If you fit a linear model to a data that is non-linearly related, the model will be incorrect and hence unreliable. When you use the model for extrapolation, you are likely to get erroneous results. Hence, you should always plot a graph of observed predicted values.

Why is OLS unbiased?

Under the standard assumptions, the OLS estimator in the linear regression model is thus unbiased and efficient. No other linear and unbiased estimator of the regression coefficients exists which leads to a smaller variance. An estimator is unbiased if its expected value matches the parameter of the population.

Under which assumptions is the OLS estimator consistent?

The OLS estimator is consistent when the regressors are exogenous, and—by the Gauss–Markov theorem—optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated.

What if regression assumptions are violated?

If the X or Y populations from which data to be analyzed by linear regression were sampled violate one or more of the linear regression assumptions, the results of the analysis may be incorrect or misleading. For example, if the assumption of independence is violated, then linear regression is not appropriate.

What OLS assumption is violated by omitted variables bias?

The following OLS assumption is most likely violated by omitted variables​ bias: are unbiased and consistent.

Is OLS unbiased?

OLS estimators are BLUE (i.e. they are linear, unbiased and have the least variance among the class of all linear and unbiased estimators). Amidst all this, one should not forget the Gauss-Markov Theorem (i.e. the estimators of OLS model are BLUE) holds only if the assumptions of OLS are satisfied.

Why is OLS the best?

The Gauss-Markov theorem states that satisfying the OLS assumptions keeps the sampling distribution as tight as possible for unbiased estimates. The Best in BLUE refers to the sampling distribution with the minimum variance. That’s the tightest possible distribution of all unbiased linear estimation methods!