Question 2. Consider the regression model yr = 1 +B2xt +er . A researcher uses EViews and data for 24 years to obtain the following results: Dependent Variable: Y Method: Least Squares Date: 04/15/2011 Time: 9:29 Sample: 1 24 Included observations: 24 Variable Coefficient Std. Error t-Statistic Prob. 3.502697 0.282881 12.38223 0.0000 XO -1.611587 0.155536 -10.36153 0.0000 R-squared 0.829934 Mean dependent var 0.634268 Adjusted R-squared 0.822203 S.D. dependent var 0.675885 S.E. of regression 0.284993 Akaike info criterion 0.406953 Sum squared resid 1.78686 Schwarz criterion 0.505124 Log likelihood 2.883440 F-statistic 107.3612 Durbin-Watson stat 1.089621 Prob(F-statistic) 0.000000 Dependent Variable: RESID Method: Least Squares Date: 04/15/2011 Time: 9:34 Sample: 1 24 Included observations: 24 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob C 0.029408 0.260307 0.112973 0.9111 -0.013921 0.143077 -0.097298 0.9234 RESID(-1) 0:448692 0.199726 2.246539 0.0356 R-squared 0.193763 Mean dependent var -1.08E-15 Adjusted R-squared 0.116979 S.D. dependent var 0.278729 S.E. of regression 0.261919 Akaike info criterion 0.274909 Sum squared resid 1.440637 Schwarz criterion 0.422166 Log likelihood -0.298906 F-statistic 2.523469 Durbin-Watson stat 2.057325 Prob(F-statistic) 0.104198Semester Two Final Examination 2023 ECN 3202 Applied Econometrics a) Conduct a Lagrange Multiplier test of the null hypothesis that the' errors are serially uncorrelated against the alternative that they follow an AR(1) process with p # 0. Use a 1% level of significance. Make sure you write out the null and alternative hypotheses in terms of p, the decision rule, the test statistic and your decision