Ql. [26 points]
Consider a simple linear regression (SLR) model which satisfies Gauss Markov assumptions:
Y = f0 + fi1 X + u, where the error term u has an unconditional variance of a.2.
Let ì be a linear estimator of ß1, i.e. can be expressed as wY1, where n is the sample size and the w coefficients are functions of sample data of the independent variable X. Let n 3 in this question for simplicity.
(a) (6 points) Derive an expression for the conditional expectation of . What condition(s) must w1, w2, and w3 satisfy for to be an unbiased estimator? (sec note below)
(b) (6 points) Derive an expression for the conditional variance of . (see note below)
(c) (2 points) Prove the identity 2X — X — Xk = 3 (X — 2), where I can be 1, 2, or 3, and k will take on the other two values. For example, if j = I, then j and k take on values of 2 and 3, hence 2X1 — X2 — X3 = 3 (X1 —2). Or if j = 2, then] and k take on values of 1 and 3, hence 2X2 — X1 — X3 = 3(X2—X).
(d) (12 points) Make use of your answer in parts (a), (b), and (c), find the formula of w1, w2, and w3 that makes the estimator with the minimum variance among all linear unbiased estimators. Try your best to simplify the expressions for w1, w2, and w3.
Hint for part (d): This is essentially an optimization problem under constraint in which you want to minimize the conditional variance under the constraint that the linear estimator is unbiased. Use of basic calculus is needed (e.g. =k X’). Although you will encounter some long expressions in the process of solving this problem, you will discover that many terms cancel out and the solution has a rather simple structure. Be patient.
Note: “Conditional” here means conditional on sample data of the independent variable X.
Please identify which Gauss Markov assumptions are needed where appropriate in your answer.
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