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1. The estimation of a Cobb-Douglas production function for 20 firms of a given industry yields: q = 16.907 + 0.322ki + 2.77711 R2 =
1. The estimation of a Cobb-Douglas production function for 20 firms of a given industry yields: q = 16.907 + 0.322ki + 2.77711 R2 = 0.915 DW = 2.032 RSS = 0.461 Use a = 5% Var = 0.166070 0.031857 0.216639 -0.857446 -0.057229 4.939971 where a fitted values of naturals logaritrhms of output (output express in 1000 tons); ki: natural logarithms of capital output (output express in 1000 tons) li: natural logaritrhms of labour (labour is in hours) RSS: residual sum of squares 1: firm index Var : variance - covariance matrix of estimates a. Evaluate the statistical significance of the coefficients. b. A regression on the original regressors, @e? and a constant term yields the following statistics: RP = 0.296041 F=1.177507 coeff of qe has a t-statistic of 2.876 (i) With this information, which test can you implement to deal with the problem omitted variables and why? (ii) Implement the test as stated in b(i) and interpret the results. (iii) What is (are) five consequence(s) of omitted variables in a regression model? 1. The estimation of a Cobb-Douglas production function for 20 firms of a given industry yields: q = 16.907 + 0.322ki + 2.77711 R2 = 0.915 DW = 2.032 RSS = 0.461 Use a = 5% Var = 0.166070 0.031857 0.216639 -0.857446 -0.057229 4.939971 where a fitted values of naturals logaritrhms of output (output express in 1000 tons); ki: natural logarithms of capital output (output express in 1000 tons) li: natural logaritrhms of labour (labour is in hours) RSS: residual sum of squares 1: firm index Var : variance - covariance matrix of estimates a. Evaluate the statistical significance of the coefficients. b. A regression on the original regressors, @e? and a constant term yields the following statistics: RP = 0.296041 F=1.177507 coeff of qe has a t-statistic of 2.876 (i) With this information, which test can you implement to deal with the problem omitted variables and why? (ii) Implement the test as stated in b(i) and interpret the results. (iii) What is (are) five consequence(s) of omitted variables in a regression model
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