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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