Question

Multiple choice!

Consider the model Yi = B0 + B1X1i + B2X2i + B3X3i + B4X4i + Ui. To test the null hypothseis of B2 = B3 = 0, the restricted regression is:

- A. Yi = B0 + B1X1i + B2X2i + B3X3i + B4X4i + Ui
- B. Yi = B0 + Ui
- C. Yi = B0 + B1X1i + B4X4i + Ui
- D. Yi = B0 + B2X2i + B3X3i + Ui

Consider the model Yi = B0 + B1X1 + B2X2 + B3(X1X2) + Ui. This model assumes that:

- A. X2 is the regressor and X1 is the regressand
- B. the effect of X2 on Y depends on the value of X1
- C. the effect of X2 on Y is constant
- D. there is no possible effect of X2 on Y

Consider the quadratic regression Yi = B0 + B1Xi + B2(Xi-squared) + Ui. If you could not reject a null hypothesis that the slope coefficient B2 is not significant, then:

- A. the polynomial degree will be increased.
- B. the quadratic regression will become a linear regression.
- C. we can claim that B1 is not significant also.
- D. the quadratic regression will become a cubic regression.

Consider the quadratic regression Yi = B0 + B1Xi + B2(Xi-squared) + Ui. The degree of polynomial in this regression is:

- A. 0
- B. 2
- C. 1
- D. 2 squared or (4)

Answer #1

1)Consider the model Yi = B0 + B1X1i + B2X2i + B3X3i + B4X4i + Ui. To test the null hypothseis of B2 = B3 = 0, the restricted regression is:

As B2 and B3 coefficient need to check. only their coefficient need to be included.

**D. Yi = B0 + B2X2i + B3X3i + Ui**

2)Consider the model Yi = B0 + B1X1 + B2X2 + B3(X1X2) + Ui. This model assumes that:

**B. the effect of X2 on Y depends on the value of
X1**

3)Consider the quadratic regression Yi = B0 + B1Xi + B2(Xi-squared) + Ui. If you could not reject a null hypothesis that the slope coefficient B2 is not significant, then:

**B. the quadratic regression will become a linear
regression.**

**4)**Consider the quadratic regression Yi = B0 +
B1Xi + B2(Xi-squared) + Ui. The degree of polynomial in this
regression is:

**B. 2**

**Please revert in case of any doubt.**

**Please upvote. Thanks in advance**

Consider the model, Yi = B0 + B1 X1,i + B2 X2,i + Ui, where
sorting the residuals based on the X1,i and X2,i gives: X1 X2
Goldfeld-Quandt Statistic 1.362 (X1) 4.527 (X2) If there is
heteroskedasticity present at the 5% critical-F value of 1.624,
then choose the most appropriate heteroskedasticity correction
method.
A. Heteroskedastic correction based on X2.
B. Heteroskedastic correction based on X1.
C. No heteroskedastic correction needed.
D. White's heteroskedastic-consistent standard errors
E. Not enough information.

Suppose the true model is
Yi = B0 + B1 Xi +
B2Zi + ui
but you estimate the model:
Yi = a0 + a1Xi+
ui
If Z is positively correlated with Y and negatively correlated
with X, a1 will be a positively biased estimate of
B1.
Explain.

1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this
regression using OLS and get the following results: b0=-3.13437;
SE(b0)=0.959254; b1=1.46693; SE(b1)=21.0213; R-squared=0.130357;
and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and
b1, respectively. The total number of observations is
2950.According to these results the relationship between C and Y
is:
A. no relationship
B. impossible to tell
C. positive
D. negative
2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this...

Given the following regression model: Y = B0 + B1X1 + B2X2 +
B3X3 + u
If we want to test the null hypothesis H0: B0 + B1 = 5, which of
the following is correct?
1) Estimate Y = gamma0 + gamma1(X1 - 1) + gamma2X2 + gamma3X3 +
u and test the hypothesis gamma0 = 5
2) Estimate Y = gamma0 + gamma1(X1 + 1) + gamma2X2 + gamma3X3 +
u and test the hypothesis gamma1 = 5...

Consider the data below:
x1
x2
y
-1
1
1
-1
2
3
-1
3
6
1
1
2
1
1
3
1
3
8
a) Fit the model Y = B0 + B1x1
+ B2x2 +
B3x22 + error. For i = 1,2,...,6,
error is normal, with a mean of 0 and a variance of
sigma2.
b) Test if B3 < 1, at a 5% level of
significance.

1. Consider the bivariate model: Yi = β0+β1Xi+ui . Explain what
it means for the OLS estimator, βˆ 1, to be consistent. (You may
want to draw a picture.)
2. (Circle all that applies) Which of the following regression
functions is/are linear in the parameters a) Yi = β1 + β2 1 Xi b)
Yi = β1 + β 3 2Xi c) Yi = β1 + β2Xi

Consider the following model:
?i= B0 + B1D1i + B2 D2i + ui
where ?1i= (0 if person is nongraduate and 1 if person is
graduate)
and D2i = (0 if person is graduate and 1 if person is non
graduate)
and yi denotes the monthly salary.
(a) What is the problem with this model?
(b) How are you going to fix the problem? Suggest two ways to
fix the problem.

consider the following model:
sleep= B0+B1total hourswok+ B2 education+ B3 female+ B4 age+ B5
age2 +E
What is a regression that would allow the variance of E to be
different between females and males. The variance should not rely
upon other factors.

1. Consider the following linear regression model which
estimates only a constant:
Yi = β1 + ui
What will the value of ˆβ1 be? Remember we are minimizing the sum
of the squared residuals.
2. Consider the following regression model with K parameters:
Yi = β1 + β2X2i + β3X3i + ... + βKXKi + ui
Now consider the F-test of the null hypothesis that all slope
parameters (β2,β3,...,βK) are equal to zero. Using the equation
from class:
F =（(RSSk...

7)
Consider the following regression model
Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + β5X5i + ui
This model has been estimated by OLS. The Gretl output is
below.
Model 1: OLS, using observations 1-52
coefficient
std. error
t-ratio
p-value
const
-0.5186
0.8624
-0.6013
0.5506
X1
0.1497
0.4125
0.3630
0.7182
X2
-0.2710
0.1714
-1.5808
0.1208
X3
0.1809
0.6028
0.3001
0.7654
X4
0.4574
0.2729
1.6757
0.1006
X5
2.4438
0.1781
13.7200
0.0000
Mean dependent var
1.3617
S.D. dependent...

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 4 minutes ago

asked 21 minutes ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 3 hours ago