In the simple linear regression model , the Gauss Markov ( classical ) assumptions guarantee that the OLS estimator of the unknown parameters is BLUE. Among those assumptions , in order to have consistency of the OLS estimator we need ( this is a question on the necessary condition ): Question 4Select one: a. we need that the errors are not correlated with each others and that they have zero mean b. we need that the errors are homoscedastic ( all have the same variance) c. we need that the residuals are not correlated with the explanatory variables and that the residuals have zero mean d. we need that the errors have zero mean and that they are not correlated with the regressors ( no endogeneity) e. we need that the errors are homoscedastic ( all have the same variance) and not correlated with each others ( no serial correlation)
In the simple linear regression model , the Gauss Markov ( classical ) assumptions guarantee that the OLS estimator of the unknown parameters is BLUE. Among those assumptions , in order to have consistency of the OLS estimator we need ( this is a question on the necessary condition ):
Question 4Select one:
we need that the errors are not correlated with each others and that they have zero
we need that the errors are homoscedastic ( all have the same variance)
we need that the residuals are not correlated with the explanatory variables and that the residuals have zero mean
we need that the errors have zero mean and that they are not correlated with the regressors ( no endogeneity)
we need that the errors are homoscedastic ( all have the same variance) and not correlated with each others ( no serial
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