SECTION TWO: MULTIPLE CHOICE OUEST For the ha forca question the appropriate 6 4. In the equation consumption + income, what with consumption $500 and fitted value of consumption 475 A. 5975 B 550 C 525 D. $300 7 5. The Gauss-Markov theorem will NOT hold if A. the error term has an expected value of nere given any value of the issu B. the error term has the same variance given any value of the explanatory the regression model relies on the method of random sampling for collectie D. the error term is correlated with the regressions 6. Running regressions where each explanatory variable is estimated as a fr explanatory variables can help detect A omitted relevant variables B. irelevant variables included c. multicollinearity D. heteroskedasiticity 7. What are the consequences of using OLS when heteroskedasticity is present? A. The OLS estimators are still BLUE B. Confidence intervals and hypothesis testing are invalid, C All coefficient estimators are based. D. It requires very large sample sizes to get efficient estimators. 8. If your OLS estimated output includes an F-statistic and p-value for the overall signific regression model,how should you interpret the pvalue? A. The probability that all of the coefficients are actually equal to zero B. The probability that all of the coefficients other than the intercept are actually zero and we would the estimated results. C. The probability that the model is completely invalid. D. The probability that the model is incorrectly specified. 9. Inclusion of an irrelevant variable as a regressor is not so harmful because: A. the BLUE property of OLS estimators is not affected. B. the OLS estimators remain unbiased. C the OLS estimators are still efficient. D. the OLS estimators are uniform INS Won in the worstel 1 5 with consumption - S500 and Cuted value of w4157 10 le the equation consumption comes to the A. $975 RS50 C 525 D. $300 5. The Gauss-Markov theorem will NOT how A. the error term has an expected value of to given my value of the B. the error term has the same variance gives any value of the capatory the regression model relies on the method of random sampling for collection of des D. the error term u is correlated with the regressos 6. Running regressions where each explanatory variable is estimated as a function of the explanatory variables can help detect A. omitted relevant variables. B. irrelevant variables included. C. multicollinearity D. heteroskedasiticity 7. What are the consequences of using OLS when heteroskedasticity is presen! A. The OLS estimators are still BLUE B. Confidence intervals and hypothesis testing are invalid. C. All coefficient estimators are biased. D. It requires very large sample sizes to get efficient estimators. 8. If your OLS estimated output includes an F-statistic and p-value for the overall significance of the regression model, how should you interpret the p-value? A. The probability that all of the coefficients are actually equal to zero B. The probability that all of the coefficients other than the intercept are actually zero and we would observe the estimated results. C. The probability that the model is completely invalid. D. The probability that the model is incorrectly specified. 9. Inclusion of an irrelevant variable as a regressor is not so harmful because: A. the BLUE property of OLS estimators is not affected. B. the OLS estimators remain unbiased. C. the OLS estimators are still efficient. D. the OLS estimators are uniform. 7 4. In the equation consumption = + B, Income + what is the real for the bation with consumption$500 and fitted value of consumption4757 10 2. A. $975 B. $50 C. $25 D. S300 20 2 5. The Gauss-Markov theorem will NOT hold if A. the error term has an expected value of zero given any value of the independent variables B. the error term u has the same variance given any value of the explanatory variables C. the regression model relies on the method of random sampling for collection of diatas D. the error term u is correlated with the regressors 6. Running regressions where each explanatory variable is estimated as a function of the remaining explanatory variables can help detect A. omitted relevant variables. B. irrelevant variables included. C. multicollinearity. D. heteroskedasiticity. 7. What are the consequences of using OLS when heteroskedasticity is present? A. The OLS estimators are still BLUE. B. Confidence intervals and hypothesis testing are invalid. C. All coefficient estimators are biased. D. It requires very large sample sizes to get efficient estimators. 8. If your OLS estimated output includes an F-statistic and p-value for the overall significance of the regression model, how should you interpret the p-value? A. The probability that all of the coefficients are actually equal to zero. B. The probability that all of the coefficients other than the intercept are actually zero and we would observe the estimated results. C. The probability that the model is completely invalid. D. The probability that the model is incorrectly specified. 9. Inclusion of an irrelevant variable as a regressor is not so harmful because: A. the BLUE property of OLS estimators is not affected. B. the OLS estimators remain unbiased. C. the OLS estimators are still efficient. D. the OLS estimators are uniform. SECTION TWO: MULTIPLE CHOICE OUEST For the ha forca question the appropriate 6 4. In the equation consumption + income, what with consumption $500 and fitted value of consumption 475 A. 5975 B 550 C 525 D. $300 7 5. The Gauss-Markov theorem will NOT hold if A. the error term has an expected value of nere given any value of the issu B. the error term has the same variance given any value of the explanatory the regression model relies on the method of random sampling for collectie D. the error term is correlated with the regressions 6. Running regressions where each explanatory variable is estimated as a fr explanatory variables can help detect A omitted relevant variables B. irelevant variables included c. multicollinearity D. heteroskedasiticity 7. What are the consequences of using OLS when heteroskedasticity is present? A. The OLS estimators are still BLUE B. Confidence intervals and hypothesis testing are invalid, C All coefficient estimators are based. D. It requires very large sample sizes to get efficient estimators. 8. If your OLS estimated output includes an F-statistic and p-value for the overall signific regression model,how should you interpret the pvalue? A. The probability that all of the coefficients are actually equal to zero B. The probability that all of the coefficients other than the intercept are actually zero and we would the estimated results. C. The probability that the model is completely invalid. D. The probability that the model is incorrectly specified. 9. Inclusion of an irrelevant variable as a regressor is not so harmful because: A. the BLUE property of OLS estimators is not affected. B. the OLS estimators remain unbiased. C the OLS estimators are still efficient. D. the OLS estimators are uniform INS Won in the worstel 1 5 with consumption - S500 and Cuted value of w4157 10 le the equation consumption comes to the A. $975 RS50 C 525 D. $300 5. The Gauss-Markov theorem will NOT how A. the error term has an expected value of to given my value of the B. the error term has the same variance gives any value of the capatory the regression model relies on the method of random sampling for collection of des D. the error term u is correlated with the regressos 6. Running regressions where each explanatory variable is estimated as a function of the explanatory variables can help detect A. omitted relevant variables. B. irrelevant variables included. C. multicollinearity D. heteroskedasiticity 7. What are the consequences of using OLS when heteroskedasticity is presen! A. The OLS estimators are still BLUE B. Confidence intervals and hypothesis testing are invalid. C. All coefficient estimators are biased. D. It requires very large sample sizes to get efficient estimators. 8. If your OLS estimated output includes an F-statistic and p-value for the overall significance of the regression model, how should you interpret the p-value? A. The probability that all of the coefficients are actually equal to zero B. The probability that all of the coefficients other than the intercept are actually zero and we would observe the estimated results. C. The probability that the model is completely invalid. D. The probability that the model is incorrectly specified. 9. Inclusion of an irrelevant variable as a regressor is not so harmful because: A. the BLUE property of OLS estimators is not affected. B. the OLS estimators remain unbiased. C. the OLS estimators are still efficient. D. the OLS estimators are uniform. 7 4. In the equation consumption = + B, Income + what is the real for the bation with consumption$500 and fitted value of consumption4757 10 2. A. $975 B. $50 C. $25 D. S300 20 2 5. The Gauss-Markov theorem will NOT hold if A. the error term has an expected value of zero given any value of the independent variables B. the error term u has the same variance given any value of the explanatory variables C. the regression model relies on the method of random sampling for collection of diatas D. the error term u is correlated with the regressors 6. Running regressions where each explanatory variable is estimated as a function of the remaining explanatory variables can help detect A. omitted relevant variables. B. irrelevant variables included. C. multicollinearity. D. heteroskedasiticity. 7. What are the consequences of using OLS when heteroskedasticity is present? A. The OLS estimators are still BLUE. B. Confidence intervals and hypothesis testing are invalid. C. All coefficient estimators are biased. D. It requires very large sample sizes to get efficient estimators. 8. If your OLS estimated output includes an F-statistic and p-value for the overall significance of the regression model, how should you interpret the p-value? A. The probability that all of the coefficients are actually equal to zero. B. The probability that all of the coefficients other than the intercept are actually zero and we would observe the estimated results. C. The probability that the model is completely invalid. D. The probability that the model is incorrectly specified. 9. Inclusion of an irrelevant variable as a regressor is not so harmful because: A. the BLUE property of OLS estimators is not affected. B. the OLS estimators remain unbiased. C. the OLS estimators are still efficient. D. the OLS estimators are uniform