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