1. You are interested the causal effect of X1 on Y, B1. Suppose that X1 and X2 are uncorrelated. You estimate B, by regressing Y onto X1 (so that X2 is not included in the regression). Does this estimator suifer 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 X1, X2i, ...Xi has a mean of zero (b) (Xi, X21... Y), i = 1,., n are independently and identically distributed (c) Heteroskedasticity (d) Perfect multicollinearity

1. You are interested the causal effect of X1 on Y, B1. Suppose that X1 and X2 are uncorrelated. You estimate B, by regressing Y onto X1 (so that X2 is not included in the regression). Does this estimator suifer 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 X1, X2i, ...Xi has a mean of zero (b) (Xi, X21... Y), i = 1,., n are independently and identically distributed (c) Heteroskedasticity (d) Perfect multicollinearity

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

See similar textbooks

Similar questions

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…
5 We are given a sample of n observations which satisfies the following regression model: yi = β0 + β1xi1 + β2xi2 + ui , for all i = 1, . . . , n. This model fulfills the Least-Squares assumptions plus homoskedasticity. (a) Explain how you would obtain the OLS estimator of the coefficients {β0, β1, β2} in this model. (You do not need to show a full proof. Writing down the relevant conditions and explain)
2. In a multiple regression of y on x1, x2, and x3, including additional variables on the right-hand side of the model always increases R2. (True/False).
4. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). 5. Re-write the specified model in (a) with values from the regression results and interpret the coefficients.
Discuss the FIVE (5) importance of adding error term in the regression model.
(2)What would the consequence be for a regression model if theerrors were not homoscedastic?
The OLS estimators of the coefficients in multiple regression will have omitted variable bias: a. i only if an omitted determinant of b. if an omitted variable is correlated with at least one of the regressors, even though it is not a determinant of the dependent variable. C. only if the omitted variable is not normally distributed. d. if an omitted determinant of is a continuous variable. Y; i is correlated with at least one of the regressors. e. if the degree of freedom is less than 50.
1. An analyst ran a regression with four predictor variables. Variable description Variable Name Salary in R1000.00 Years at company Age in years Education in years SALARY YEARS AGE EDYEARS He suspects that AGE can be dropped from the model and he decided to employ forward stepwise regression. Show all the steps he has to do to get to a fitted response regression without age. 2. BIC, Bayesian information criteria or SBC, Schwarz' Bayesian Criteria, are the same. Give the aquations for AIC and BIC and explain the difference in these two equations in terms of the terms in the equations as well as the consequences. 3. Give a short description of measuring the actual predictive capabilities of the selected regression. model.
1. For a regression model y = XB + u where u is N(0, o?1), y is nx1 matrix, X is nxp matrix, B is px1 matrix and u is nx1 matrix, a. derive the estimators B using the method of least squares
11. Which of the following statements is not true about multicollinearity? (a) Perfect multicollinearity will prevent you from being able to estimate a linear regression model. (b) Imperfect mulitcollinearity affects the individual t-statistics of the regressors. (c) Multicollinearity is defined as a linear relationship between different independent variables. (d) Imperfect multicollinearity affects model validity of the model. (e) The least squares estimators are unbiased in the presence of imperfect multicollinearity.
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…
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 OO