Question

From the annual data for the U.S. manufacturing sector for 1899–1922, Dougherty obtained the following regression results:
log Y = 2.81 − 0.53 log K + 0.91 log L + 0.047t
se = (1.38) (0.34) (0.14) (0.021) (1)
R2 = 0.97 F = 189.8
where Y = index of real output, K = index of real capital input, L = index of real labor input, t = time or trend.
Is there multicollinearity in regression, How do you know? (1)
Interpret regression. What is the role of the trend variable in this regression? (1)
What is the role of the K variable in this regression? (1)
Any time series regression containing more than four independent variables results in garbage.” Critically evaluate the statement. (1)

Answer 1

Answer:

the main concept for this question is maths and maths should be very good

Answer 2

10.24. From the annual data for the U.S. manufacturing sector for 1899–1922, Dougherty obtained the following regression results†:

l---og Y = 2.81 − 0.53 log K + 0.91 log L + 0.047t

se = (1.38) (0.34)

(0.14)

(0.021)

(1)

R2 = 0.97

F = 189.8

where Y = index of real output, K = index of real capital input, L = index of real labor input, t = time or trend.

10.24. From the annual data for the U.S. manufacturing sector for 1899–1922, Dougherty obtained the following regression results†:l—og Y = 2.81 − 0.53 log K + 0.91 log L + 0.047t se = (1.38) (0.34)(0.14)(0.021)(1) R2 = 0.97F = 189.8 where Y = index of real output, K = index of real capital input, L = index of real labor input, t = time or trend.