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
Problem 1P
icon
Related questions
Question
Problem 13
algebra, take the expectation treating x3 and ₁1 as nonrandom.]
12 The following equation represents the effects of tax revenue mix on subsequent employment growth
for the population of counties in the United States:
growth
where growth is the percentage change in employment from 1980 to 1990, sharep is the share of prop-
adini serty taxes in total tax revenue, share, is the share of income tax revenues, and shares is the share of
sales tax revenues. All of these variables are measured in 1980. The omitted share, share, includes
fees and miscellaneous taxes. By definition, the four shares add up to one. Other factors would include
expenditures on education, infrastructure, and so on (all measured in 1980).
Why must we omit one of the tax share variables from the equation?
Give a careful interpretation of B₁.
(i)
(ii)
13 (1)
=
Bo + Bisharep + B₂share, + B3shares + other factors,
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
15 anos
³₁ = ( (z − 2)y.)/(2(2₁ − 2)x.).
-
Show that B₁ is linear and unbiased. Remember, because E(ulx) = 0, you can treat both x, and zi
as nonrandom in your derivation.
(ii) Add the homoskedasticity assumption, MLR.5. Show that
(E80.
Var(3₁) = ²(2(z, - 2)²)/(2(2, - 2)x.)".
(iii) Show directly that, under the Gauss-Markov assumptions, Var(3₁) ≤ Var(B₁), where B₁ is the
OLS estimator. [Hint: The Cauchy-Schwartz inequality in Math Refresher B implies that
(n-¹(²₁-
(2,-2)(x − 3))² = (n-¹(2₁ - 2)²)(n-¹(x - 1)³):
notice that we can drop x from the sample covariance.]
Transcribed Image Text:algebra, take the expectation treating x3 and ₁1 as nonrandom.] 12 The following equation represents the effects of tax revenue mix on subsequent employment growth for the population of counties in the United States: growth where growth is the percentage change in employment from 1980 to 1990, sharep is the share of prop- adini serty taxes in total tax revenue, share, is the share of income tax revenues, and shares is the share of sales tax revenues. All of these variables are measured in 1980. The omitted share, share, includes fees and miscellaneous taxes. By definition, the four shares add up to one. Other factors would include expenditures on education, infrastructure, and so on (all measured in 1980). Why must we omit one of the tax share variables from the equation? Give a careful interpretation of B₁. (i) (ii) 13 (1) = Bo + Bisharep + B₂share, + B3shares + other factors, 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 15 anos ³₁ = ( (z − 2)y.)/(2(2₁ − 2)x.). - Show that B₁ is linear and unbiased. Remember, because E(ulx) = 0, you can treat both x, and zi as nonrandom in your derivation. (ii) Add the homoskedasticity assumption, MLR.5. Show that (E80. Var(3₁) = ²(2(z, - 2)²)/(2(2, - 2)x.)". (iii) Show directly that, under the Gauss-Markov assumptions, Var(3₁) ≤ Var(B₁), where B₁ is the OLS estimator. [Hint: The Cauchy-Schwartz inequality in Math Refresher B implies that (n-¹(²₁- (2,-2)(x − 3))² = (n-¹(2₁ - 2)²)(n-¹(x - 1)³): notice that we can drop x from the sample covariance.]
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 4 steps

Blurred answer
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman