13 (1) Consider the simple regression model y = Bo + B₁x + u under the first four Gauss-Markov assumptions. For some function g(x), for example g(x) = x² or g(x) = log(1 + x²), define Z₁ = g(x). Define a slope estimator as mes show anoder B₁ = ( (z − 2)x))/(2(z. - 2)x). - Show that B₁ is linear and unbiased. Remember, because E(ulx) = 0, you can treat both Xi as nonrandom in your derivation. (ii) Add the homoskedasticity assumption, MLR.5. Show that and Zi Var(3,) = o²($(z, − 2)²)/(2(2, − 2)x;). (iii) Show directly that, under the Gauss-Markov assumptions, Var(3₁) ≤ Var(B₁), where B, is the OLS estimator. [Hint: The Cauchy-Schwartz inequality in Math Refresher B implies that (n-¹2 (2,₁ − 2)(x − 3))² = (n-¹ (z − 2)²)(n-¹ § (x, − 1)²);
13 (1) Consider the simple regression model y = Bo + B₁x + u under the first four Gauss-Markov assumptions. For some function g(x), for example g(x) = x² or g(x) = log(1 + x²), define Z₁ = g(x). Define a slope estimator as mes show anoder B₁ = ( (z − 2)x))/(2(z. - 2)x). - Show that B₁ is linear and unbiased. Remember, because E(ulx) = 0, you can treat both Xi as nonrandom in your derivation. (ii) Add the homoskedasticity assumption, MLR.5. Show that and Zi Var(3,) = o²($(z, − 2)²)/(2(2, − 2)x;). (iii) Show directly that, under the Gauss-Markov assumptions, Var(3₁) ≤ Var(B₁), where B, is the OLS estimator. [Hint: The Cauchy-Schwartz inequality in Math Refresher B implies that (n-¹2 (2,₁ − 2)(x − 3))² = (n-¹ (z − 2)²)(n-¹ § (x, − 1)²);
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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