Why is OLS called Blue?

OLS estimators are BLUE (i.e. they are linear, unbiased and have the least variance among the class of all linear and unbiased estimators). If the OLS assumptions are satisfied, then life becomes simpler, for you can directly use OLS for the best results – thanks to the Gauss-Markov theorem!

What is blue in regression?

The Gauss Markov theorem says that, under certain conditions, the ordinary least squares (OLS) estimator of the coefficients of a linear regression model is the best linear unbiased estimator (BLUE), that is, the estimator that has the smallest variance among those that are unbiased and linear in the observed output …

What does blue mean in econometrics?

best linear unbiased estimator
The best linear unbiased estimator (BLUE) of the vector of parameters is one with the smallest mean squared error for every vector of linear combination parameters.

Why is OLS so named?

1 Answer. Least squares in y is often called ordinary least squares (OLS) because it was the first ever statistical procedure to be developed circa 1800, see history.

Why we use OLS model?

In data analysis, we use OLS for estimating the unknown parameters in a linear regression model. The goal is minimizing the differences between the collected observations in some arbitrary dataset and the responses predicted by the linear approximation of the data. We can express the estimator by a simple formula.

Why is OLS a good estimator?

The OLS estimator is one that has a minimum variance. This property is simply a way to determine which estimator to use. An estimator that is unbiased but does not have the minimum variance is not good. An estimator that is unbiased and has the minimum variance of all other estimators is the best (efficient).

What is Endogeneity in OLS?

In econometrics, endogeneity broadly refers to situations in which an explanatory variable is correlated with the error term. The problem of endogeneity is often, unfortunately, ignored by researchers conducting non-experimental research and doing so precludes making policy recommendations.

What is Exogeneity and Endogeneity?

Exogeneous: A variable is exogenous to a model if it is not determined by other parameters and variables in the model, but is set externally and any changes to it come from external forces. Endogenous: A variable is endogenous in a model if it is at least partly function of other parameters and variables in a model.