Question 1

The Var(aX +BY) equals to
(a) Var(X) + Var(Y)
(b) aVar(X) + 8Var(Y) + E(XY)
(c) a?Var(X) +9aVar(Y) +2aẞCov(X,Y)
(d) zero if X and Y are independent.

Accepted Answer

c

Question 2

An estimate
(a) is a random variable.
(b) has an expectation and a population variance. 
(c) can have different values in different samples. 
(d) is a calculation rule based no a sample.

Accepted Answer

c

Question 3

An estimator is consistent if
(a) ti si unbiased sa the sample size increases.
(b) it folows na exact Normal distribution.
(c) its variance goes ot zero as the sample size increases.
(d) (a) and (c) hold together.

Accepted Answer

d

Question 4

Which of the folowing si aresult of multicolinearity?
(a) Asmal R-squared value.
(b) A smal F-value.
(c) Alarge standard error fo the regression.
(d) Large standard erors of regression coefficients.

Accepted Answer

d

Question 5

Given a simple regression, which answersi one fohte thre standard SI assumptions?
(a) u; si normally distributed. (b) E(2)X.) = .0
(c) var(4|X.) si constant.
(d) There si no perfect multicollinearity

Accepted Answer

b

Question 6

estimator BLUE meaning?
(a) Among the class of linear unbiased estimators, ti si the one with the smalest variance.
(b) gives Best Linear Unbiased Estimate.
(c) is the BL Uniform Estimator.
(d) Among al unbiased estimators, one smalest variance.

Accepted Answer

a

Question 7

OLS large sample, preferred use robust standard errors, when uncertain whether
(a) the conditional variance of erors is constant. 
(b) multicolinearity is not problem.
(c) observations are identically distributed. 
(d) large outliers unlikely.

Accepted Answer

a

Question 8

Aer statements I and I rtue for asimple regression model?
:I fI X; and u; are jointly normaly distributed, then X; and Y, are jointly normaly distributed. I:l fI X; and Y, aer jointly normaly distributed, then large outliers of X; and Y, are unlikely.

Accepted Answer

1 true
2 true

Question 9

I and 2 true for a simple regression model? When
1 correlation u and X negative, OLS-estimator slope negative bias.
2 there si one omited variable Z, and Z neg efect on dep variable and Z negatively corelated X, OlS-estimator slope neg bias.

Accepted Answer

1 true
2 false

Question 10

A dummy variable si used as an independent variable ni a regression model when
(a) the variable involved si numerical.
(b) hte variable involvedsi categorical, taking only wto values. (c) when two independent variables interact.
(d) None of the above

Accepted Answer

b

Question 11

Y, = BX+u and alternative estimator B=y/x Suppose LS-assumptions and of homoskedasticity hold then: The estimator is
(a) biased
(b) consistent but not unb
(e) consistent, unb and efficient
(d) unb, but the OLS estimator has asmaler variance

Accepted Answer

d

Question 12

Suppose that aregression sufers from the Dummy Variable Trap. Which fo the folowing si correct? 
(a) The OLS estimators do not exist.
(b) The OLS estimators wil be inconsistent. (c) The OLS estimators are inefficient.
(d) The OLS estimators are biased.

Accepted Answer

a

Question 13

R2 tells wheter
a chosen most appropriate set regressors
b the regressors are good at predicting observed values of dependent variable
C regressors are true cause of movements in dependent variable 
d an included variable is statistically sig

Accepted Answer

b

Question 14

For a regression ti si given that n = 40, k= ,4 SSR = 262 and TSS = 815. The absolute difference between the values of R-squared and the adjusted R-squared si
(a) 0.03297. (b) 0.03572.
(e) 0.03674. (d) 0.04592.

Accepted Answer

c

Question 15

Omitted variable bias occurs if:
:I The omitted variable is uncorrelated with the variable of interest.
I: The omitted variable does not have a causal effect on the dependent variable.

Accepted Answer

d

Question 16

apply OLS to a multiple regression model, rob t-test can decide whether the coef of X, is nonzero. For validity of test,required that
X, cor with dep variable
X, has causal effect on dep variable
X uncor with error term
X uncor other indep variables

Accepted Answer

c

Question 17

ass Y=B0+B1X+u satisfies LS ass. you regress X on Y and a constant (inverse regression), and covert the obtained OLS-estimates to estimate B0 and B1. Whichcorrect?
inconsistent estimatest(LSASS not hold applied inverse reg

Accepted Answer

d

Econometrics Midterm

practice exams

Question	Answer
The Var(aX +BY) equals to (a) Var(X) + Var(Y) (b) aVar(X) + 8Var(Y) + E(XY) (c) a?Var(X) +9aVar(Y) +2aẞCov(X,Y) (d) zero if X and Y are independent.	c
An estimate (a) is a random variable. (b) has an expectation and a population variance. (c) can have different values in different samples. (d) is a calculation rule based no a sample.	c
An estimator is consistent if (a) ti si unbiased sa the sample size increases. (b) it folows na exact Normal distribution. (c) its variance goes ot zero as the sample size increases. (d) (a) and (c) hold together.	d
Which of the folowing si aresult of multicolinearity? (a) Asmal R-squared value. (b) A smal F-value. (c) Alarge standard error fo the regression. (d) Large standard erors of regression coefficients.	d
Given a simple regression, which answersi one fohte thre standard SI assumptions? (a) u; si normally distributed. (b) E(2)X.) = .0 (c) var(4\|X.) si constant. (d) There si no perfect multicollinearity	b
estimator BLUE meaning? (a) Among the class of linear unbiased estimators, ti si the one with the smalest variance. (b) gives Best Linear Unbiased Estimate. (c) is the BL Uniform Estimator. (d) Among al unbiased estimators, one smalest variance.	a
OLS large sample, preferred use robust standard errors, when uncertain whether (a) the conditional variance of erors is constant. (b) multicolinearity is not problem. (c) observations are identically distributed. (d) large outliers unlikely.	a
Aer statements I and I rtue for asimple regression model? :I fI X; and u; are jointly normaly distributed, then X; and Y, are jointly normaly distributed. I:l fI X; and Y, aer jointly normaly distributed, then large outliers of X; and Y, are unlikely.	1 true 2 true
I and 2 true for a simple regression model? When 1 correlation u and X negative, OLS-estimator slope negative bias. 2 there si one omited variable Z, and Z neg efect on dep variable and Z negatively corelated X, OlS-estimator slope neg bias.	1 true 2 false
A dummy variable si used as an independent variable ni a regression model when (a) the variable involved si numerical. (b) hte variable involvedsi categorical, taking only wto values. (c) when two independent variables interact. (d) None of the above	b
Y, = BX+u and alternative estimator B=y/x Suppose LS-assumptions and of homoskedasticity hold then: The estimator is (a) biased (b) consistent but not unb (e) consistent, unb and efficient (d) unb, but the OLS estimator has asmaler variance	d
Suppose that aregression sufers from the Dummy Variable Trap. Which fo the folowing si correct? (a) The OLS estimators do not exist. (b) The OLS estimators wil be inconsistent. (c) The OLS estimators are inefficient. (d) The OLS estimators are biased.	a
R2 tells wheter a chosen most appropriate set regressors b the regressors are good at predicting observed values of dependent variable C regressors are true cause of movements in dependent variable d an included variable is statistically sig	b
For a regression ti si given that n = 40, k= ,4 SSR = 262 and TSS = 815. The absolute difference between the values of R-squared and the adjusted R-squared si (a) 0.03297. (b) 0.03572. (e) 0.03674. (d) 0.04592.	c
Omitted variable bias occurs if: :I The omitted variable is uncorrelated with the variable of interest. I: The omitted variable does not have a causal effect on the dependent variable.	d
apply OLS to a multiple regression model, rob t-test can decide whether the coef of X, is nonzero. For validity of test,required that X, cor with dep variable X, has causal effect on dep variable X uncor with error term X uncor other indep variables	c
ass Y=B0+B1X+u satisfies LS ass. you regress X on Y and a constant (inverse regression), and covert the obtained OLS-estimates to estimate B0 and B1. Whichcorrect? inconsistent estimatest(LSASS not hold applied inverse reg	d

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