A key assumption for the identification of the ceteris paribus effect in a multiple linear regression model, Yt = XtB+ €t Homoskedasticity: Zero unconditional mean: Zero conditional mean: Simple random samplin: V (€₂) = 0² E(et |Xt) = 0 E(et| Xt) = 0 Cov(Et, Es) = 0
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- 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.Addition of explanatory variables in a regression model increases the value of _____. Select one: a. explained sum of squares (ESS) b. residual sum of squares (RSS) c. total sum of squares (TSS) d. Both TSS and ESSThe assumption that the error terms in a regression model follow the normal distribution with zero mean and constant variance is required Select one: Estimation of the regression model using OLS method Point estimation of the parameters Hypothesis testing and inference Inference One of the assumption of CLRM is that the number of observations in the sample must me greater the number of Select one: Dependent and independent variable Dependent Variable Regressor Regressands Information about numerical values of variables from period to period is Select one: Time series data Pooled data Panel data Cross section data Multicollinearity is essentially a Select one: Sample phenomenon or population phenomenon sample phenomenon Sample phenomenon and population phenomenon Population phenomenon The larger the standard error of the estimator, the greater is the uncertainty of estimating the true value of the unknown parameters. This statement is Select one: True False
- Enumerate the 10 assumptions of the classical linear regression model (CLRM) and discuss its importance in econometrics analysis.The appropriate statistic to examine the goodness of fit of a two-variable regression model is _____. Select one: a. t-statistic b. F-statistic c. R2-statistic d. Chi-square statisticThe estimated regression models having a different number of explanatory variables are compared on the basis of _____. Select one: a. Chi squared -statistic b. Adjusted R squared-statistic c. R squared-statistic d. None of the above
- Choosing the best nearest neighbor regression model means 1. picking the number of nearest neighbors K that minimizes the sum of squared residuals. 2. picking the number of nearest neighbors K that minimizes the test mean squared error. 3. picking the number of nearest neighbors K that minimizes the training error rate. 4. picking the number of nearest neighbors K that minimizes the training mean squared error.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.State the Ordinary Least-Squares assumptions of the regression model with one regressor.
- Which 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 constantConsider the regression model Yi = β0 + β1X1i + β2X2i + β3(X1i * X2i) +ui. a. ΔY>/ΔX1 = β1 + β3X2 (effect of change in X1, holding X2 constant).b. ΔY/ΔX2 = β2 + β3X1 (effect of change in X2, holding X1 constant).c. If X1 changes by ΔX1 and X2 changes by ΔX2, then ΔY =(β1+β3X2)ΔX1 + (β2 + β3X1)ΔX2 + β3ΔX1ΔX2.The overall significance of an estimated multiple regression model is tested by using _____. Select one: a. F-test b. t-test c. χ^2-test d. None of the above