Jan 2020 Flashcards Preview

Econometrics > Jan 2020 > Flashcards

Flashcards in Jan 2020 Deck (20)
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1
Q

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

A

True

2
Q

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

A

False

3
Q

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

A

False

4
Q

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

A

True

5
Q

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

A

True

6
Q

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

A

False

7
Q

Serial correlation in the residuals implies bias in coefficient estimates

A

False

8
Q

Heteroscedasticity in the residuals implies OLS is inefficient

A

True

9
Q

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

A

False

10
Q

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

A

False

11
Q

Biased estimator always has higher mean-square error than unbiased

A

False

12
Q

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

A

True

13
Q

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

A

True

14
Q

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

A

False

15
Q

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

A

False

16
Q

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

A

True

17
Q

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

A

True

18
Q

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

A

False

19
Q

Heteroscedasticity is most often found in time series regression models

A

False

20
Q

Heteroscedasticity can often be dealt with by scaling the data appropriately

A

True