The extended least squares assumptions in the multiple regression model include four assumptions from Chapter 6 (u_i has conditional mean zero; (X_i,Y_i), i = 1,…, n are i.i.d. draws from their joint distribution; X_i and u_i have nonzero finite fourth moments; there is no perfect multicollinearity). In addition, there are two further assumptions, one of which is A)heteroskedasticity of the error term. B)serial correlation of the error term. C)the conditional distribution of u_i given X_i is normal. D)invertibility of the matrix of regressors.

Question

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The conditional distribution of u_i given X_i is assumed to be normal, which implies that the errors have constant variance and are normally distributed. This assumption is necessary to conduct hypothesis tests and construct confidence intervals for the regression coefficients.

The Extended Least Squares Assumptions in the Multiple Regression Model