What problem with an OLS regression is instrumental variables intended to address? Failure of assumption MLR.1 due to non-linear econometric model. Failure of assumption MLR.5 due to heteroskedastic error. Failure of assumption MLR.4 due to endogenous variables. Failure of assumption MLR.2 due to endogenous variables.
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What problem with an OLS regression is instrumental variables intended to address?
Failure of assumption MLR.1 due to non-linear econometric model.
Failure of assumption MLR.5 due to heteroskedastic error.
Failure of assumption MLR.4 due to endogenous variables.
Failure of assumption MLR.2 due to endogenous variables.
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- 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. InconsistentWhich of the following is a consequence of severe multicollinearity in a regression model? A. High standard errors for the estimated coefficientsB. Lower standard errors for the estimated coefficientsC. The OLS estimator becomes biasedD. The dependent variable becomes constantQUESTION 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 one of the following is NOT an assumption of the classical linear regression model (CLRM)? Select one: a. The disturbance terms are independent of one another. b. The dependent variable is not correlated with the disturbance terms. c. The explanatory variables are uncorrelated with the error terms. d. The disturbance terms have zero mean.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.what are the key features , Strength and limitation of following model? and when which model should be used? Ordinary Least Squares Logit regression model Probit regression model
- In a regression problem with 1 output variable and with a total number of 100 possible input variables, what is the number of all possible models with three input variables?If the value of Durbin-Watson test statistic (d) for the classical linear regression model is close to 2, then _____. Select one: a. the model does not suffer from heteroscedasticity problem b. the error term does not follow a first-order autoregressive process c. the model does not suffer from autocorrelation problem d. the error term in non-stationaryA) State whether the following statement(s) are true/false with justification i) If the model is linear in parameters, but there is non-linear relation between dependent and independent variables, OLS cannot be used. ii) OLS can be applied to estimate a multiple linear regression model, and each slope coefficient shows the full effect of an individual variable on the dependent variable iii) Both slope and intercept will change if dependent and independent variables are divided by factor k (=1000). B) In a left-tailed test where you reject Ho only in the lower tail, what is the p-value if Z = (-) 1.00?
- Consider a multiple regression model: Y = Bi + B2X + B3W + B4Z + u, where Y is a dependent variable, X, W, and Z, are regressors, and u is a disturbance term. Suppose X = 2W + Z. Then the following is true: O The model suffers from nonlinearity in parameters O None of the presented possible answers are correct. O The model suffers from the lack of degrees of freedom O The model suffers from perfect collinearity O The model suffers from autocorrelationStyles 6. The following two regression models are Probit and Logit respectively: Pr(Y=1|X)=(Bo+Bi xXr+Bzx X2) Pr(Y=1|X)=F(Bu + B₁ × Xs + B₂ × X2) (a) what functions do and F represent? (b) How do Probit, and Logit ensure that the predicted probabilities are always between 0 and 1? (c) Sketch a graph of the Y= $(Z) function. (Z on the horizontal axis, Y on the vertical axis) (d) What estimation method is used to estimate the coefficients in a Probit/Logit model? (e) What are the two measures of fit for models with binary dependent variables? Focus 88 B EAs the number of relevant independent variables in a regression increases, the R-squared of a regression Select one: a. exhibits greater heteroskedasticity b. increases c. decreases d. stays constant