Question
In: Economics
Suppose that the true model is y = β0 +β1x+u. You decided to run the model...
Suppose that the true model is y = β0 +β1x+u. You decided to run the model without the intercept term.
1. Under what circumstance(s) would the coefficient term in your model (without the intercept term) be an unbiased estimator.
2. Your boss likes your model choice because the conditional variance of your estimator is the most efficient (between the two model choices). Evaluate your boss’s statement.
Solutions
Rahul Sunny answered 4 months agoRelated Solutions
Suppose that the true model is y = β0 +β1x+u. You decided to run the model...
Suppose that the true model is y = β0 +β1x+u. You decided to run
the model without the intercept term. a) Under what circumstance(s)
would the coefficient term in your model (without the intercept
term) be an unbiased estimator. b) Your boss likes your model
choice because the conditional variance of your estimator is the
most efficient (be- tween the two model choices). Evaluate your
boss’s statement.
You have the following regression model. y = β0 + β1x1 + β2x2 + β3x3 + u
You have the following regression model. y = β0 + β1x1 + β2x2 + β3x3 + u You are sure the first four Gauss-Markov assumptions hold, but you are concerned that the errors are heteroskedastic. How would you test for hetereskedasticity? Show step by step.
In a multiple regression Y = β0+β1X+β2D, where Y is the annual income (in dollars), X...
In a multiple regression Y = β0+β1X+β2D, where Y is the annual
income (in dollars), X is number of years of education, and D is
gender (1 for male, and 0 for female). Below is a part of the
regression output:
Coefficient p-value
Intercept 24563 0.0054
X 1565 0.0003
D 3215 0.0001
1. Interpret the coefficient of D.
2. Is there a significant difference in the annual incomes
earned by male and female?
Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...
Using 50 observations, the following regression output is
obtained from estimating y = β0 +
β1x +
β2d1 +
β3d2 + ε.
Coefficients
Standard
Error
t Stat
p-value
Intercept
−0.82
0.25
−3.28
0.0020
x
3.36
1.20
2.80
0.0074
d1
−15.41
16.75
−0.92
0.3624
d2
8.28
2.40
3.45
0.0012
a. Compute yˆy^ for x = 312,
d1 = 1, and d2 = 0; compute
yˆy^ for x = 312, d1 = 0, and
d2 = 1. (Round your answers to 2...
Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...
Using 50 observations, the following regression output is
obtained from estimating y = β0 + β1x + β2d1 + β3d2 + ε.
Coefficients Standard Error t Stat p-value Intercept −0.61 0.25
−2.44 0.0186 x 2.86 1.04 2.75 0.0085 d1 −13.09 15.40 −0.85 0.3997
d2 6.15 2.05 3.00 0.0043 a. Compute yˆ for x = 260, d1 = 1, and d2
= 0; compute yˆ for x = 260, d1 = 0, and d2 = 1. (Round your
answers to 2...
Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...
Using 50 observations, the following regression output is
obtained from estimating y = β0 +
β1x +
β2d1 +
β3d2 + ε.
Coefficients
Standard
Error
t Stat
p-value
Intercept
−0.77
0.25
−3.08
0.0035
x
3.30
1.25
2.64
0.0113
d1
−13.30
17.50
−0.76
0.4511
d2
5.45
1.25
4.36
0.0001
a. Compute yˆy^ for x = 232,
d1 = 1, and d2 = 0; compute
yˆy^ for x = 232, d1 = 0, and
d2 = 1. (Round your answers to 2...
1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and...
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...
Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get...
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)=0.0697828; 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. The
following values are relevant for assessing goodness of fit of the
estimated model with the exception of
A. 0.130357
B. 8.769363
C. 1.46693
D. none of these
1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and...
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)=0.0697828; 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. The
number of degrees of freedom for this regression is
A. 2952
B. 2948
C. 2
D. 2950
2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this
regression using OLS...
Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x + β2d1 + β3d2 + ε.
Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x + β2d1 + β3d2 + ε.
Coefficients
StandardError
t Stat
p-value
Intercept
−0.45
0.20
−2.25
0.0293
x
3.78
1.20
3.15
0.0029
d1
−11.88
16.50
−0.72
0.4752
d2
5.25
1.25
4.20
0.0001
a. Compute yˆy^ for x = 212, d1 = 1, and d2 = 0; compute yˆy^ for x = 212, d1 = 0, and d2 = 1. (Round your answers to...
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