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 + 0.047tse = (1.38) (0.34) (0.14) (0.021) (1)R2 = 0.97 F = 189.8where Y = index of real output, K = index of real capital input, L = indexof real labor input, t = time or trend.*This formula is given by R. Stone, “The Analysis of Market Demand,” Journal of the RoyalStatistical Society, vol. B7, 1945, p. 297. Also recall (7.5.6). For further discussion, see PeterKennedy, A Guide to Econometrics, 2d ed., The MIT Press, Cambridge, Mass., 1985, p. 156.†Christopher Dougherty, Introduction to Econometrics, Oxford University Press, New York,1992, pp. 159–160.Gujarati: BasicEconometrics, FourthEditionII. Relaxing theAssumptions of theClassical Model10. Multicollinearity: WhatHappens if the Regressorsare Correlated?© The McGraw-HillCompanies, 2004CHAPTER TEN: MULTICOLLINEARITY 381Using the same data, he also obtained the following regression:
log (Y/L) =-0.11 + 0.11 log (K/L) + 0.006tse = (0.03) (0.15) (0.006) (2)R2 = 0.65 F = 19.5a. Is there multicollinearity in regression (1)? How do you know?