Suppose that the conditional variance is var(uiXi) = h(Xi) where ...

[elizabeth18]

Suppose that the conditional variance is var(uiXi) = h(Xi) where  is a constant and h is a known function. The WLS estimator is
 
  A) the same as the OLS estimator since the function is known
  B) can only be calculated if you have at least 100 observations
  C) the estimator obtained by first dividing the dependent variable and regressor by the square root of h and then regressing this modified dependent variable on the modified regressor using OLS
  D) the estimator obtained by first dividing the dependent variable and regressor by h and then regressing this modified dependent variable on the modified regressor using OLS

Question 2

The extended least squares assumptions in the multiple regression model include four assumptions from Chapter 6 (ui has conditional mean zero; (Xi,Yi), i = 1,, n are i.i.d. draws from their joint distribution;
 
  Xi and ui 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) homoskedasticity of the error term.
  D) invertibility of the matrix of regressors.

Question 3

The WLS estimator is called infeasible WLS estimator when
 
  A) the memory required to compute it on your PC is insufficient.
  B) the conditional variance function is not known.
  C) the numbers used to compute the estimator get too large.
  D) calculating the weights requires you to take a square root of a negative number.

[AmberC1996]

Answer to Question 1

Answer: C

Answer to Question 2

Props to you, cheers.

Answer to Question 3

Answer: B