econometrics exam questions

the interpretation of the slope coefficient in the model Yi=B0+B1ln(Xi)+ui is as follows

A a change in X by one unit is associated with a B1 × 100% change in Y

B a 1% change in X is associated with a B1% change in Y

C 1% change in X is associated with a change in Y of 0.01 x B1

D a change in X by one unit is associated with a B1 change in Y

C 1% change in X is associated with a change in Y of 0.01 x B1

In the regression model Yi= B0+B1Xi+B2Di+B3(Xi+Di)+ui, where X is a continuous variable and D is a binary variable, B3:

A has no meaning since (XiDi)=0 when Di=0

B has a standard error that is not normally distributed even in large samples since D is not a normally distributed variable

C indicates the differences in the slopes of the 2 regressions

D indicates the slope of the regression when Di=1

C indicates the differences in the slopes of the 2 regressions

Consider the polynomial regression model of degree Yi=B0+B1Xi+B2Xi2+…+BrXir+ui. According to the null hypothesis that the regression is linear and the alternative that is a polynomial of degree r corresponds to:

A H0:B3=0…,Br=0, vs H1: all Bj does not equal 0, j=3,…, r

B H0: Br=0 bs Br does not equal 0

C H0= Br=0 vs B1 does not equal 0

D H0=B2, B3=0…, Br=0, vs H1: atleastone Bj does not equal 0, j=2,…,r

D H0=B2, B3=0…, Br=0, vs H1: atleastone Bj does not equal 0, j=2,…,r

In the expression Pr(denyi=1/IRatioi, Black)=Φ(-2.26+2.74P/I ratioi+0.71Blacki), the effect of increain the P/I ratio from 0.3 to 0.4 for a white person:

A is 6.1 percentage points

B is 2.74 percentage points

C is 0.274 percentage points

D should not be interpreted without knowledge of R squared

A is 6.1 percentage points

Maximum likelihood estimation yields the values of the coefficients that:

A minimize the sum of squared prediction errors

B come from a probability distribution and hence have to be positive

C maximize the log likelihood function

D are typically larger than those from OLS estimators

C maximize the log likelihood function

Estimation of the IV regression model:

A requires only exact identification

B allows only one endogenous regressor, which is typically correlated with the error term

C is only possible if the number of instruments is the same as the number of regressors

D requires exact identification or over identification

D requires exact identification or over identification

In the case of the simple regression model Yi=B0+B1Xi+ui, i=1…, n when X and u are correlated, then:

A OLS and TSLS produce the same estimate

B the OLS estimator is biased in small samples only

C the OLS estimator is inconsistent

D X is exogenous

C the OLS estimator is inconsistent

The two conditions for a valid instrument are:

A corr (Zi, Xi)=0 and corr (Zi, ui)=0

B corr (Zi, Xi)=0 and corr (Zi, ui) does not equal 0

C corr (Zi, Xi) does not equal 0 and corr (Zi, ui)=0

D corr (Zi, Xi) does not equal 0 and corr (Zi, ui) does not equal 0

C corr (Zi, Xi) does not equal 0 and corr (Zi, ui)=0

Instrumental Variables regression uses instruments to

A isolate movementds in X that are uncorrelated with u

B establish the mozart effect

C increase the regression R squared

D eliminate serial correlation

A isolate movementds in X that are uncorrelated with u

The regression R squared is defined as follows:

A SSR/TSS

B SSR/n-2

C ESS/TSS

C ESS/TSS

Imagine you regressed earnings of individuals on a constant, a binary variable (Male) which takes on the value 1 for males and is 0 otherwise, and another binary variable (Female) which takes on the value 1 for females and is 0 otherwise. Because females typically earn less than males, you would expect:

A both coefficients to be the same distance from the constant, one above and the other below

B this to yield a difference in means statistic

C none of the OLS estimators to exist because there is perfect multicolinearity

D the coefficient for Male to have a positive sign, and for Female a negative sign

C none of the OLS estimators to exist because there is perfect multicolinearity

The following linear hypothesis can be tested using the F test with the exception of:

A B2=1 and B3=B4/B5

B B1+B2=1 and B3=-2B4

C B2=0

D B0=B1 and B1=0

A B2=1 and B3=B4/B5