We use ------to derive the OLS betasa.Derivative and sum of residualsb.Sum of squared residualsc.Derivatived.Derivative and sum of squared residuals

pset 5.2 Prove that $\hat{\boldsymbol{\beta}}_{G I V E}$ can be derived as the OLS estimator of $\boldsymbol{\beta}$ in the linear regression model$$\boldsymbol{y}=X \boldsymbol{\beta}+\hat{U} \boldsymbol{\gamma}+\boldsymbol{\eta}$$where $\hat{U}$ is the matrix of first-stage residuals.

pset 6.1 Prove that the OLS estimator of $\boldsymbol{\beta}$ in the linear regression model with individualspecific intercepts, that is$$y_{i t}=\boldsymbol{x}_{i t}^{\prime} \boldsymbol{\beta}+\alpha_{i}+u_{i t},$$where $\alpha_{i}$ is a parameter to be estimated, for $i=1, \ldots, n$ is equal to $\hat{\boldsymbol{\beta}}_{W G}$.

Monte-Carlo Simulations can help one prove the minimum variance property of OLS betasa.Nob.Yes

One can take derivative of the Sample Regression Function in the following manner to compute the best estimates of population parameters (betas)a.Take the derivative of the sum of squared residuals with the help of sample betas and equate to population betasb.Take the derivative of the sum of squared residuals with the help of sample betas and equate to zero.

What does SSE (Sum of Squared Errors) represent in the context of regression analysis?