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kiran syed 13 questions

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  1. 1In the simple linear regression model, the regression slope
    1. Aindicates by how many percent Y increases, given a one percent increase in X.
    2. Bwhen multiplied with the explanatory variable will give you the predicted Y.
    3. Cindicates by how many units Y increases, given a one unit increase in X.
    4. Drepresents the elasticity of Y on X.
  2. 2The normal approximation to the sampling distribution of β hat 1 is powerful because
    1. Amany explanatory variables in real life are normally distributed.
    2. Bit allows econometricians to develop methods for statistical inference.
    3. Cmany other distributions are not symmetric.
    4. Dis implies that OLS is the BLUE estimator for β1.
  3. 3Multiplying the dependent variable by 100 and the explanatory variable by 100,000 leaves the
    1. AOLS estimate of the slope the same.
    2. BOLS estimate of the intercept the same.
    3. Cregression R2 the same.
    4. Dvariance of the OLS estimators the same.
  4. 4Changing the units of measurement, e.g. measuring testscores in 100s, will do all of the following EXCEPT for changing the
    1. Aresiduals
    2. Bnumerical value of the slope estimate
    3. Cinterpretation of the effect that a change in X has on the change in Y
    4. Dnumerical value of the intercept
  5. 5To decide whether the slope coefficient indicates a "large" effect of X on Y, you look at the
    1. Asize of the slope coefficient
    2. Bregression R2
    3. Ceconomic importance implied by the slope coefficient
    4. Dvalue of the intercept
  6. 6The confidence interval for the sample regression function slope
    1. Acan be used to conduct a test about a hypothesized population regression function slope.
    2. Bcan be used to compare the value of the slope relative to that of the intercept.
    3. Cadds and subtracts 1.96 from the slope.
    4. Dallows you to make statements about the economic importance of your estimate.
  7. 7The proof that OLS is BLUE requires all of the following assumptions with the exception of:
    1. Athe errors are homoskedastic.
    2. Bthe errors are normally distributed.
    3. CE(ui I Xi) = 0 .
    4. Dlarge outliers are unlikely.
  8. 8When there are omitted variables in the regression, which are determinants of the dependent variable, then
    1. Ayou cannot measure the effect of the omitted variable, but the estimator of your included variable(s) is (are) unaffected.
    2. Bthis has no effect on the estimator of your included variable because the other variable is not included.
    3. Cthis will always bias the OLS estimator of the included variable.
    4. Dthe OLS estimator is biased if the omitted variable is correlated with the included variable.
  9. 9When you have an omitted variable problem, the assumption that E(ui Xi) = 0 is violated. This implies that
    1. Athe sum of the residuals is no longer zero.
    2. Bthere is another estimator called weighted least squares, which is BLUE.
    3. Cthe sum of the residuals times any of the explanatory variables is no longer zero.
    4. Dthe OLS estimator is no longer consistent.
  10. 10The formula for the standard error of the regression coefficient, when moving from one explanatory variable to two explanatory variables,
    1. Astays the same.
    2. Bchanges, unless the second explanatory variable is a binary variable.
    3. Cchanges.
    4. Dchanges, unless you test for a null hypothesis that the addition regression coefficient is zero.

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