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- 8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OOcourses / Faculty Of Economics & Administrative Sciences / ECON309 / Finals / ECON 309 Final Exam A 4. The following types of statistical inference are used throughout econometrics, with the exception of O a. calibration. O b. estimation. Oc. confidence intervals. O d. hypothesis testing. NextHeteroskedasticity problem may arise owing to _____. Select one: a. presence of outliers in data b. model misspecification c. incorrect data transformation d. all of the above
- When the regression line passes through the origin then: O The intercept is zero. O The regression coefficient is zero. O The correlation is zero. O The association is zero. O All of the above.Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?10- What would be the consequences for the OLS estimator if heteroskedasticity is present in a regression model but ignored?* a. It will be biased b. It will be inconsistent c. It will be inefficient d. All of (a), (b), and (c) will be true
- A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. FalseIV. 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 REconometrics (forecasting) topic: Explain the difference between a nested and a non-nested model? Provideexamples.
- Introductory Econometris: A Modern Approach 4th edition, Chapter 17 Problem 1CE: What is the command in R in order to run the "White heteroskedasticity-consistent standard errors & covariance"? In other words, I would like to run the new regression with robust standard errors in it.What is Regression Model in econometrics?14. The regression R² is a measure of: a. whether or not X causes Y. b. the goodness of fit of your regression line. c. whether or not ESS> TSS. d. the square root of the correlation coefficient. 15. The OLS residuals, û, are defined as follows: a. Ŷ-Bo-B₁X; R b. Y – Bo – PıX c. (Y-Y)² d. Y₁ - Ŷ, 16. There exist a relationship test scores and the student-teacher ratio can be modeled as a linear function with an intercept of 798.9 and a slope of -3.28. A increase in the student- teacher ratio by 2 will: a. Reduces test scores by 3.28 on average b. Results in a test score of 798.9 c. Reduces test scores by 6.56 on average d. Reduces test scores by 6.56 for every school district