3. From the annual data for the US manufacturing sector Dougherty obtained the following regression results: Log Y = 2.81 – 0.53 log K + 0.91 log L+ 0.47t (1.38) (0.34) eqn 1 std. error (0.14) (0.021) Page 1 of 3 R²= 0.97 F= 198.8 Y = index of real output, K = index of real capital input, L=index of real labor input, t = time or trend. Using the same data, he also obtained the following regression: Log (Y/L) = -0.11 + 0.11 log (K/L) + 0.006t (0.03) (0.15) eqn 2 std. error (0.006) R?= 0.65 F= 19.5 Is there multicollinearity in the regression model no. 1? How do you know? In equation 1, what is the expected sign of K? Do the results conform to this expectation? Why or why not? What is the logic behind estimating equation 2? If there is multicollinearity in regression 1, has it changed in regression 2? How?

3. From the annual data for the US manufacturing sector Dougherty obtained the following regression results: Log Y = 2.81 – 0.53 log K + 0.91 log L+ 0.47t (1.38) (0.34) eqn 1 std. error (0.14) (0.021) Page 1 of 3 R²= 0.97 F= 198.8 Y = index of real output, K = index of real capital input, L=index of real labor input, t = time or trend. Using the same data, he also obtained the following regression: Log (Y/L) = -0.11 + 0.11 log (K/L) + 0.006t (0.03) (0.15) eqn 2 std. error (0.006) R?= 0.65 F= 19.5 Is there multicollinearity in the regression model no. 1? How do you know? In equation 1, what is the expected sign of K? Do the results conform to this expectation? Why or why not? What is the logic behind estimating equation 2? If there is multicollinearity in regression 1, has it changed in regression 2? How?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Differential Equations

Have you ever observed the movement of the satellites and rockets or how the planets go around the sun in their orbits? How will you determine their motion? Definitely they follow some scientific laws and these kinds of problems are framed as differential equations.

Question

3. From the annual data for the US manufacturing sector Dougherty obtained the following
regression results:
Log Y = 2.81 – 0.53 log K + 0.91 log L+ 0.47t
(1.38) (0.34)
eqn 1
std. error
(0.14)
(0.021)
Page 1 of 3
R²= 0.97 F= 198.8
Y = index of real output, K = index of real capital input,
L=index of real labor input, t = time or trend.
Using the same data, he also obtained the following regression:
Log (Y/L) = -0.11 + 0.11 log (K/L) + 0.006t
(0.03) (0.15)
eqn 2
std. error
(0.006)
R?= 0.65 F= 19.5
Is there multicollinearity in the regression model no. 1? How do you know?
In equation 1, what is the expected sign of K? Do the results conform to this
expectation? Why or why not? What is the logic behind estimating equation 2?
If there is multicollinearity in regression 1, has it changed in regression 2? How?

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