In a multiple linear regression, which of the following can cause the OLS estimators to be biased? A sample correlation coefficient of .85 between two independent variables. The presence of heteroskedasticity. Omitting an important variable i. ii. iii.
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- (2)What would the consequence be for a regression model if theerrors were not homoscedastic?What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?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. 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 multicollinearityWho Invented Instrumental Variables Regression?If a regression equation contains an irrelevant variable, the parameter estimates will be Select one: a. Consistent and unbiased but inefficient b. Consistent and asymptotically efficient but biased c. Consistent, unbiased and efficient. d. Inconsistent
- What are the four assumptions of linear regression (simple linear and multiple)?Quantile regression (QR) is different from OLS in that: a. QR estimates marginal effects at the mean values of the dependent variables. b. QR does not estimate marginal effects at the mean values of the dependent and independent variables. c. QR minimizes the sum of squared residuals to obtain the coefficient estimates. d. QR only uses the data below the quantile where the quantile regression is being estimated.In a multiple OLS regression. Does correlation between explanitory variables violate assumtion number 4 multicolliniearity? Or is it just for perfect colinearity?
- 2. Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn’t evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...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,…What is Regression Model in econometrics?