One of the properties of the OLS estimator is A)X $\hat \beta$ = 0_k₊₁. B)that the coefficient vector $\hat \beta$ has full rank. C) $X ^ { \prime }$ (Y - X $\hat \beta$ )= 0_k₊₁. D)( $X ^ { \prime }$ X)^-¹= $X ^ { \prime }$ Y

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The correct property of the OLS (Ordinary Least Squares) estimator is that the derivative of the sum of squared residuals with respect to the coefficient vector $\hat \beta$ equals zero, which is mathematically represented as $X ^ { \prime }$ (Y - X $\hat \beta$ ) = 0. This condition ensures that the OLS estimator minimizes the sum of squared residuals, making it the best linear unbiased estimator under the Gauss-Markov theorem.

One of the Properties of the OLS Estimator Is A)X β^\hat \betaβ^​

One of the Properties of the OLS Estimator Is
A)X $\hat \beta$