QUESTION 8 Having many relevant instruments: a. is good because they provide more information. D. is a problem because instead of being just identified, the regression now becomes overidentified. с. typically results in larger standard errors for the TSLS estimator. d. is not as important for inference as having the same number of endogenous variables as instruments. е. All of the above. O f. None of the above.
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- QUESTION 8 Having many relevant instruments: a. is good because they provide more information. b. is a problem because instead of being just identified, the regression now becomes overidentified. С. typically results in larger standard errors for the TSLS estimator. d. is not as important for inference as having the same number of endogenous variables as instruments. е. All of the above. O f. None of the above.Question 1) Which of the following can cause the usual OLS t statistics to be invalid (that is, not to have t distribu- tions under HO)? (i) Heteroskedasticity. (ii) A sample correlation coefficient of 95 between two independent variables that are in the model. (iii) Omitting an important explanatory variable Question 2) Which of the following can cause OLS estimators to be biased? (i) Heteroskedasticity. (ii) Omitting an important variable. (iii) A sample correlation coefficient of .95 between two independent variables both included in the model.QUESTION 7 Estimation of the IV regression model: а. is possible if the number of instruments is equal to the number of endogenous variables. b.is possible if the model is over-identified. C. is possible if the number of instruments is larger than the number of endogenous variables. d. is possible if there is exact identification. е. All of the above. O f. None of the above.
- A regression analysis of company profits and the amount of money the company spent on advertising produced a R² = 0.72. Which of these is TRUE? 1. This model can correctly predict the profit for 72% of companies. II. 72% of the variance in company profit can be accounted for by the model. III. On average, companies spend about 72% of their profits on advertising. OA. None OB. I and III OC. II only OD. III only OE. I only1. When considering a Simple Linear Regression model, a. Describe a test that is performed to decide whether there is a statistically significant linear relationship between the dependent an independent variables? b. What are the hypotheses for the test? c. What assumptions does the test make? d. What is the formula for the test statistic used in the test? e. What is the consequence of failing to reject the null hypothesis, Ho?O Given are five observations for two variables, x and y. Excel File: data14-25.xlsx 6 9 13 20 Yi 18 9. 26 23 The estimated regression equation is y = 7. 6+ 0. 9x. a. What is the value of the standard error of the estimate (to 4 decimals)? b. Test for a significant relationship by using the t test. Use a = 0.05. What is the value of the t test statistic (to 2 decimals)? What is the p-value? Use Table 2 of Appendix B. Select your answer- What is your conclusion (a = 0.05)? - Select your answer - c. Use the F test to test for a significant relationship. Use a = 0.05. Compute the value of the F test statistic (to 2 decimals). What is the p-value? Use Table 4 of Appendix B. - Select your answer- What is your conclusion? - Select your answer- 21
- IV. 得分 What information can be obtained from this summary output? a to enter = 0.05, a to remove = 0.05 Analysis of Variance Source DF Adi sS Adi MS F-Value P-Value www Regression 4 37260200 9315050 45. 23 0. 000 0. 000 0. 000 Poten 1 4727687 4727687 22. 95 AdvExp 4630364 4630364 22. 48 Share 1 3009401 3009401 14. 61 0.001 Accounts 1 2129972 2129972 10. 34 0.004 Error 20 4119349 205967 Total 24 41379549 R-sq R-sq (adi) R-sq (pred) 453. 836 90. 04% 88. 05% 85. 97% Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -1442 424 -3. 40 0,003 Poten 0. 03822 0. 00798 4. 79 0. 000 1. 83 AdvExp 0. 1750 0. 0369 4. 74 0. 000 1. 15 Share 190. 1 49. 7 3. 82 0.001 1.74 Accounts 9. 21 2. 87 3. 22 0. 004 1. 99 Fits and Diagnostics for Unusual Observations Std Obs Sales Fit Resid Resid www 10 4876 3942 934 2. 14 RWhen the regression error is heteroskedastic, all of the following statements are false, with the exception of: a. the conditional variance of the error term is not constant. b. the OLS estimator is unbiased but not consistent. C. the OLS estimator is still BLUE.Y 70 12 50 9 57 60 14 43 9 52 11 i. Find the estimators for Bi and B2 correct to decimal points and fit the regression equation for X and Y when X is the explanatory variable. Interpret the results from the obtained equation. calculate the sum of error squared. Find the variance of the sum square error ii. iii. iv. Find the standard error for B2 Find the coefficient of correlation and give its interpretation V. vi.
- Consider a linear causal model Ya+BX+yW+u, with cov(X, W) > 0. Suppose we do not observe the variable W and have to omit it from the regression, then O OLS is expected to be larger than 3 in large samples. BOLS is expected to be equal to 3 in large samples. OLS is expected to be smaller than 3 in large samples. Since we do not know whether X and u are correlated and the sign of y, there is not enough information to compare OLS and B.Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?Regression Statistics Multiple R 0.971 R-Square A Adjusted R-Square .942 Standard Error 30.462 Observations 51 ANOVA df SS MS F Significance F Regression C 747851.57 373925.79 402.98 9.89E-31 Residual 48 D 927.91 Total 50 792391.11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept E 62.13 26.79 1.60E-30 1539.66 1789.51 Price of Roses −6.68 F −1.41 1.64E-01 −16.16 2.81 Disposable Income (M) 9.73 0.34 G 1.23E-31 9.04 10.42…