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. Oc. 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.
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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.QUESTION 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,…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: Oa. 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. d. 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.
- 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. nageYi = 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-specification1. 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
- QUESTION 2 Continue to use the example from Question 1. Suppose each product is randomly assigned to a process by a computer program, but some products get reassigned on the factory floor (for practical reasons). Let Z¡ denote the original assignment and X¡ the actual process used to produce i. In a regression of Y¡ on X¡ and Wj, OLS is: d. Potentially biased because W; should not be included b. Potentially biased, but an IV regression using Z¡ as an instrument can be used to obtain a consistent estimator C. Unbiased because the products were randomly assigned in the beginning d. Unbiased as long as Zj is also included as a control variableWhich 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.Can I include the dummy variables in regression equation like Y=a+bX+u where the X is the vector of x variables that contain dummy variables with 5 categories? how should I write my general regression equation with this?
- 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…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 0.4581. Can you estimate a regression model for Y and X? 2. What are the assumptions of the model in 1? 3. Do you think that this model is accurate? 4. What are the related hypotheses of 37 5. Discuss and interpret your results. Y 4 4 -1 -2 11 -2 -4 0. 3. 7. 12 -3 -6 7. 14