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Econometrics > Jan 2020 > Flashcards

Flashcards in Jan 2020 Deck (20)
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1

OLS slope coefficient equals ratio of sample covariance of X and Y to sample variance of X

True

2

OLS intercept is that value of X consistent with Y = 0

False

3

For OLS to be unbiased, all 4 Gauss-Markov assumptions must hold

False

4

The R^2 for a regression will always increase with extra variables

True

5

The omission of a relevant variable will normally lead to bias in the remaining coefficient estimates

True

6

In a log-linear regression equation the elasticity of Y with respect to X is calculated by multiplying the slope coefficient by X-bar/Y-bar

False

7

Serial correlation in the residuals implies bias in coefficient estimates

False

8

Heteroscedasticity in the residuals implies OLS is inefficient

True

9

If Durbin-Watson statistic is < 2 it indicates negative serial correlation

False

10

In bivariate regression the F statistic is equal to the t statistic for the slope coefficient

False

11

Biased estimator always has higher mean-square error than unbiased

False

12

OLS residuals are by construction uncorrelated with exogenous variables of regression equation

True

13

The reason why OLS coefficient estimates usually follow t-distribution rather than normal is that the error variance is usually unknown

True

14

If we wish to test hypothesis that a coefficient is positive then we use a two-tailed test

False

15

The standard error of the regression always lies in the range 0 to 1 and the closer it is to 1 the better the fit of the model

False

16

The F test for joint significance of rhs variables is distributed as F with degrees of freedom T - k - 1 where T is number of observations, and k is number of slope coefficients

True

17

Serial correlation of errors in a regression model would not, in itself, lead us to expect OLS coefficient estimates to be biased

True

18

If DW test stat lies between upper and lower critical bounds then we should reject the null that there is no serial correlation

False

19

Heteroscedasticity is most often found in time series regression models

False

20

Heteroscedasticity can often be dealt with by scaling the data appropriately

True