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You estimate a regression of the excess return on Microsoft stock (ermsoft) on a constant, the excess return on the S&P market index (ersandp) and the return on the term spread (rterm)

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You estimate a regression of the excess return on Microsoft stock (ermsoft) on a constant, the excess return on the S&P market index (ersandp) and the return on the term spread (rterm). You run a diagnostic test. EViews provides you with the following output Dependent Variable: ERMSOFT Method: Least Squares Sample: 1986M04 2013M04 Included observations: 325 Variable Coefficient Std. Error t-Statistic Prob. ? ERSANDP RTERM FITTED^2 -1.177958 1.454598 4.005786 0.013618 0.797167 0.174896 2.497635 0.009268 -1.477680 8.316935 1.603832 1.469306 0.1405 0.0000 0.1097 0.1427 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.198864 Mean dependent var 0.191377 S.D. dependent var 12.64684 Akaike info criterion 51341.54 Schwarz criterion -1283.800 Hannan-Quinn criter. 26.56033 Durbin-Watson stat 0.000000 -0.263150 14.06400 7.924923 7.971493 7.943509 2.136191 Which of the following do you conclude? Select one: A. There is no evidence of incorrect functional form at the 5 percent level. B. There is no evidence of autocorrelation at the 5 percent level. C. There is no evidence of heteroskedasticity at the 5 percent level. D. None of these. E. There is no evidence of multicollinearity at the 5 percent level.
Consider the following model yt = 0.3 +0.4€-1 + Et, where &t is a white noise process. The correlation between yand yt-1 for this process is: Select one: A. 0.4 O B. 0.3448 C. None of these. D. 0.12 O E. 1.3333