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a. The model has a multicollinearity problem and needscorrection; the model has an autocorrelation problem and needscorrection.
b. The model has a multicollinearity problem but doesnot need correction; the model has an autocorrelation problem andneeds correction.
c. The model has a multicollinearity problem and needscorrection; the model does not have an autocorrelationproblem.
Method: Least Squares Sample: 1959 2005 Included observations: 47 Variable с X1 X2 X3 X4 X5 X6 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Variable Vif test Variance Inflation Factors Sample: 1959 2005 Included observations: 47 c123456 с X1 X2 X3 X4 X5 Coefficient t-Statistic -11304.74 9963.786 -1.134583 87.45286 6.649853 -0.001467 0.000154 -1.432391 -1.066252 0.643255 -91.29435 X6 Std. Error Prob. 0.2633 0.0000 0.0000 0.101101 -14.16789 0.0000 0.367530 -2.901130 0.0060 0.083757 7.680038 0.0000 199.5108 -0.457591 0.6497 13.15110 -9.558925 0.999398 Mean dependent var 0.999307 S.D. dependent var 645.9589 Akaike info criterion 16690518 Schwarz criterion -367.0249 Hannan-Quinn criter. 11061.62 Durbin-Watson stat 0.000000 Coefficient Uncentered Variance VIF 99277023 11182.44 44.22054 366.3096 2.36E-08 0.010221 0.135078 0.007015 39804.57 101822.8 24543.81 15.91595 16.19151 16.01965 0.707944 Centered VIF ΝΑ 75.89698 84.37724 33.87871 52.41870 5.122013 78.85589 5.170066 24882.94 872.4650 3407.490 824.9711