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.
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- (2)What would the consequence be for a regression model if theerrors were not homoscedastic?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 OODescribe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
- A researcher estimates a regression using two different software packages.The first uses the homoskedasticity-only formula for standard errors. Thesecond uses the heteroskedasticity-robust formula. The standard errors arevery different. Which should the researcher use? Why?1.1 Which of the following is NOT a good reason for including a disturbance term in a regression equation?/ A. To allow for random influences on the dependent variable/ B. To allow for errors in the measurement of the dependent variable/ C. It captures omitted determinants of the dependent variable D. To allow for the non-zero mean of the dependent variable/ 1.2 Consider the equation Y = B1 + B2X2 + u. A null hypothesis of H0: B2 = 0 means that/ A. X2 has no effect on the expected value of Y / B. B2 has no effect on the expected value of Y/ C. X2 has no effect on the expected value of B2 / D. Y has no effect on the expected value of X2/ 1.3 The OLS residuals in the multiple regression model/ A. can be calculated by subtracting the fitted values from the actual values / B. are zero because the predicted values are another name for forecasted values / C. are typically the same as the population regression function errors / D. cannot be calculated because there…1. You are interested the causal effect of X on Y, B1. Suppose that X, and X2 are uncorrelated. You estimate B1 by regressing Y onto X1 (so that X2 is not included in the regression). Does this estimator suffer from omitted variable bias due to the exclusion of X2? (a) Yes (b) No (c) Maybe 2. Omitted variable bias violates which of the following assumptions: (a) The conditional distribution of u, given X1i X2i, ...Xki has a mean of zero (b) (Xi, X2i...Y;), i = 1, ., n are independently and identically distributed (c) Heteroskedasticity (d) Perfect multicollinearity
- (Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..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 R
- As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…Exlplain Linear Conditionally Unbiased Estimators and the Gauss–Markov Theorem with its limitations?Discuss the FIVE (5) importance of adding error term in the regression model.