Question 1

Weighted least-squares is efficient when E[ui2] $\neq$ E[uj2] = $\sigma$2 for all i$\neq$ j.

Accepted Answer

A) True 
 B)False  True

Question 2

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-Draw a diagram indicative of negative serial correlation. Be sure to label the axes.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 3

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-Describe the Hildreth-Lu Scanning procedure.

Accepted Answer

Since $\rho$ is expected to lie between plus and minus unity, a computer can easily check to see which value of $\rho$ gives the best results in a GLS regression.Assume $\rho$ = -1; Run GLS; Assume $\rho$ = -.9; Run GLS; Assume $\rho$ = -.8; Run GLS; ...; Assume $\rho$ = +.9; Run GLS; Assume $\rho$ = +1; Run GLS. Of all these GLS regressions, use the one that has the best fit according to the R² or SER.

Question 4

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-Describe the weighted least-squares procedure.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 5

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-Describe the maximum likelihood procedure.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 6

The Durbin-Watson statistic is invalid in autoregressive models, models without a constant term, and models with n = 9.

Accepted Answer

A) True 
 B)False

Question 7

R-squared from a GLS regression is directly comparable to the R-squared from the same regression estimated using OLS.

Accepted Answer

A) True 
 B)False

Question 8

Serial correlation is when E[ui uj] = 0 for all i $\neq$ j.

Accepted Answer

A) True 
 B)False

Question 9

R-squared is biased downward in a regression suffering from serial correlation.

Accepted Answer

A) True 
 B)False

Question 10

$\begin{array} {| l| } \hline \text { AVG } = 77 \ \text { RANGE: } 30 - 100 \ \ \text { A - } 4 \ \text { B - } \ \text { C - 2 } \ \text { D - } \ \text { F - 2 } \ \hline \end{array}$ -Given Y_i = $\hat { \beta } _ { 0 }$ + $\hat { \beta } _ { 1 }$ X1_i + $\hat { \beta } _ { 2 }$ X2_i + e_i, describe the White test for heteroskedasticity making sure to specify the auxiliary regression in this case.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 11

A regression with a Durbin-Watson statistic close to 4 most likely suffers from negative autocorrelation.

Accepted Answer

A) True 
 B)False

Question 12

Application of the Newey-West technique will alter the estimates of the P-values on the structural parameters.

Accepted Answer

A) True 
 B)False

Question 13

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-What do you recommend in a situation where GLS does not remedy autocorrelation? Explain.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 14

The error terms (ut) from a regression are white noise when ut ~ N(0, $\sigma$ 2).

Accepted Answer

A) True 
 B)False

Question 15

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-Are the standard errors of the coefficients in a regression expected to increase or decrease after Newey-West is applied? Explain why?

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 16

In the presence of autocorrelated error terms, GLS yields BLUE parameter estimates.

Accepted Answer

A) True 
 B)False

Question 17

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-What is generalized least-squares?

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 18

$\begin{array} {| l| } 
\hline
\text { AVG } = 77 \
\text { RANGE: } 30 - 100 \
\
\text { A - } 4 \
\text { B - } \
\text { C - 2 } \
\text { D - } \
\text { F - 2 }
\
\hline
\end{array}$
-Describe 3 ways to estimate ? in a generalized least-squares regression.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Question 19

Autocorrelation increases the probability of a TYPE II error on a test of significance on a given structural parameter.

Accepted Answer

A) True 
 B)False

Question 20

$\begin{array} {| l| } \hline \text { AVG } = 77 \ \text { RANGE: } 30 - 100 \ \ \text { A - } 4 \ \text { B - } \ \text { C - 2 } \ \text { D - } \ \text { F - 2 } \ \hline \end{array}$ -Given Y_i = $\hat { \beta } _ { 0 }$ + $\hat { \beta } _ { 1 }$ X1_i + $\hat { \beta } _ { 2 }$ X2_i + $\hat { \beta } _ { 2 }$ X3_i + e_i, write the auxiliary regression required to perform the Park test for heteroskedasticity.

Accepted Answer

The answer of \(\begin{array} {| l| } 
\hline
\text { AVG...

Exam 5: Multiple Regression

Weighted least-squares is efficient when E[u_i²] $\neq$ E[u_j²] = $\sigma$ ² for all i $\neq$ j.

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -Draw a diagram indicative of negative serial correlation. Be sure to label the axes.

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -Describe the Hildreth-Lu Scanning procedure.

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -Describe the weighted least-squares procedure.

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -Describe the maximum likelihood procedure.

The Durbin-Watson statistic is invalid in autoregressive models, models without a constant term, and models with n = 9.

R-squared from a GLS regression is directly comparable to the R-squared from the same regression estimated using OLS.

Serial correlation is when E[u_i u_j] = 0 for all i $\neq$ j.

R-squared is biased downward in a regression suffering from serial correlation.

A regression with a Durbin-Watson statistic close to 4 most likely suffers from negative autocorrelation.

Application of the Newey-West technique will alter the estimates of the P-values on the structural parameters.

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -What do you recommend in a situation where GLS does not remedy autocorrelation? Explain.

The error terms (u_t) from a regression are white noise when u_t~ N(0, $\sigma$ ²).

In the presence of autocorrelated error terms, GLS yields BLUE parameter estimates.

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -What is generalized least-squares?

$\begin{array} {| l| } \hline\text { AVG } = 77 \\\text { RANGE: } 30 - 100 \\\\\text { A - } 4 \\\text { B - } \\\text { C - 2 } \\\text { D - } \\\text { F - 2 }\\\hline\end{array}$ -Describe 3 ways to estimate ? in a generalized least-squares regression.

Autocorrelation increases the probability of a TYPE II error on a test of significance on a given structural parameter.

Exam 5: Multiple Regression

Weighted least-squares is efficient when E[ui2] ≠\neq= E[uj2] = σ\sigmaσ 2 for all i ≠\neq= j.

The Durbin-Watson statistic is invalid in autoregressive models, models without a constant term, and models with n = 9.

R-squared from a GLS regression is directly comparable to the R-squared from the same regression estimated using OLS.

Serial correlation is when E[ui uj] = 0 for all i ≠\neq= j.

R-squared is biased downward in a regression suffering from serial correlation.

A regression with a Durbin-Watson statistic close to 4 most likely suffers from negative autocorrelation.

Application of the Newey-West technique will alter the estimates of the P-values on the structural parameters.

The error terms (ut) from a regression are white noise when ut ~ N(0, σ\sigmaσ 2).

In the presence of autocorrelated error terms, GLS yields BLUE parameter estimates.

Autocorrelation increases the probability of a TYPE II error on a test of significance on a given structural parameter.

Weighted least-squares is efficient when E[u_i²] $\neq$ E[u_j²] = $\sigma$ ² for all i $\neq$ j.

Serial correlation is when E[u_i u_j] = 0 for all i $\neq$ j.

The error terms (u_t) from a regression are white noise when u_t~ N(0, $\sigma$ ²).