Which of the following is NOT TRUE in describing the assumptions for the classical linear regressions, and the reasons why such assumptions are necessary? Select one: O a. There is no correlation between the error term and the independent variables. This is required for unbiasedness. O b. The error term has a constant covariance. This is required for efficiency. O c. The error term is statistically independent of one another. This is required for unbiasedness. Od. The error term has a zero mean. This is required for unbaisedness. Oe. The error term follows a normal distribution. This is required for parameter testing.
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- Which of the following statements regarding omitted variable bias problem is true? Select one: a. Omitted variable bias happens when there are exogenous independent variables in the model. O b. Omitted variable bias is only an issue when the sample size is small. c. Omitted variable bias happens when you don't include in the regression all the factors affecting the outcome. d. Omitted variable bias happens when a factor in the error term is related to an independent variable.If you included both time and entity fixed effects in the regression model which includes a constant, then: O a. you have perfect multicollinearity. O b. you can exclude the constant and one entity binary variable to estimate the model. you can exclude the constant and one time binary variable to estimate the model. O d. you can exclude one time binary variable and one entity binary variable to estimate the model. O e. All of the above. Of. None of the above.Which of the following statements about the R-squared statistic of a linear estimation (or linear regression) is true? An R-squared statistic of 0.6 means the variation of the independent (explanatory) variable explains about 60% of the variation of the dependent variable. O An R-squared statistic of 0.6 means 60% the variation of the dependent variable cannot be explained by the variation of the independent (explanatory) variable. O An R-squared statistic of 0.6 means the variation of the dependent variable explains about 60% of the variation of the independent (explanatory) variable.
- Yi = B1 + B2xi2 + · · · + Brxik + ei, i = 1, . .., N, var (e; X) = var (y:|X) = o? Which property of linear regression model is most appropriate for the above regression? Select one: O a. heteroskedasticity O b. strict exogeneity c. autocorrelation O d. model mis-specificationQUESTION 1 In the equation, y = 8o + Bjx1 + 8zx2 + u, 8z is a(n) O a. intercept parameter O b. slope parameter O. dependent variable O d. independent variable QUESTION 2 If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of O a. perfect collinearity O b.heteroskedasticty O . homoskedasticity O d. omitted variable bias QUESTION 3 Which of the following is true of R 2? O a. R- usually decreases with an increase in the number of independent variables in a regression. O b.R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables. OC.A low R2 indicates that the Ordinary Least Squares line fits the data well. O d. R² is also called the standard error of regression. QUESTION 4 We estimate the model Wage, = -2.91+0.568educ; + 0.033 exper; +0.115 tenure; by OLS, where wage is the hourly wage of a worker measured in dollars,…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
- 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…d/My courses / Faculty Of Economics & Administratiive Sciences / ECON309 / Finals / ECON 309 Fin 13. In the simple linear regression model, the regression slope a. O a. indicates by how many percent Y increases, given a one percent increase in X. ut of O b. represents the elasticity of Y on X. uestion Oc. when multiplied with the explanatory variable will give you the predicted Y. O d. indicates by how many units Y increases, given a one unit increase in X. nageConsider the regression model Yi=bot Bi Xitui Suppose that you know Bo = 0. Derive the formula for the least squares estimator of B₁. The least squares objective function is O A. n O B. O C. O D. E (Yi-bo-b1Xi) i=1 n Σ (Yi-bo-biXi) i=1 2 n Σ (v₁²-bo-b₁x₁²) i=1 n E (Yi-bo-b+Xi) 3 i=1
- The modern interpretation of regression says that regression... O A. studies the statistical dependence of a regressor on a regressand with the view of estimating the mean value of the former in terms of the fixed values of the latter. O B. studies the causal link between the dependent variable and independent variable (or variables). C. studies the statistical dependence of a regressand on a regressor (or regressors) with the view of estimating the precise relationship between variables in order to forecast changes in the relationship between the variables. O D. studies whether there is statistical independence of a dependent variable in relation to an independent variable with the view of estimating statistical dependence. E. studies the statistical dependence of an outcome variable on an exogenous variable (or variables) with the view of estimating the average response of the regressand for given values of predictor variables.The interpretation of the slope coefficient in the model In (Y₁) = Bo + B₁ln (X;) + μ; is as follows: O A. a 1% change in X is associated with a change in Y of 0.01 B₁. O B. a change in X by one unit is associated with a 100 B₁ % change in Y. O C. a change in X by one unit is associated with a ₁ change in Y. D. a 100% change in X is associated with a 100 ₁ % change in Y.2)According to VAR regression results given below, what can you say about the nature of causality (Use 5 % level for significance)? Comment on the causal relationship between Y and X by justification. Use Granger Causality methodology to determine the validity of a causal relationship. Also, comment on why the Granger Causality test is appropriate for the table below to investigate the causal relationship between Y and X. Dependent Variable: Y Variable Lag Y 1 Y 2 X 1 X 2 0.083 Test for joint significance, Dependent Variable: Y Variable Significance Level 0.002 0.123 Y X Significance Level 0.002 0.009 0.012 Dependent Variable: X Variable Lag Significance Level X X Y Y Test for joint significance, Dependent Variable: X Variable Significance Level 1 2 1 2 X Y 0.012 0.056 0.087 0.045 0.046 0.458