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 Yi = $\hat { \beta } _ { 0 }$ + $\hat { \beta } _ { 1 }$ X1i + $\hat { \beta } _ { 2 }$ X2i + ei, describe the White test for heteroskedasticity making sure to specify the auxiliary regression in this case.

Accepted Answer

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

(1) OLS Technique: Suppose the residuals

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 Yi = $\hat { \beta } _ { 0 }$ + $\hat { \beta } _ { 1 }$ X1i + $\hat { \beta } _ { 2 }$ X2i + $\hat { \beta } _ { 2 }$ X3i + ei, write the auxiliary regression required to perform the Park test for heteroskedasticity.

Accepted Answer

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

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Draw a diagram indicative of negative serial correlation. Be sure to label the axes.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Describe the Hildreth-Lu Scanning procedure.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Describe the weighted least-squares procedure.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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.

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.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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).

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Are the standard errors of the coefficients in a regression expected to increase or decrease after Newey-West is applied? Explain why?

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

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -What is generalized least-squares?

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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.

The Nature of Econometrics

Simple Regression Analysis

Residual Statistics

Hypothesis Testing

Alternate Functional Forms

Filters

Exam 5: Multiple Regression

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

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Draw a diagram indicative of negative serial correlation. Be sure to label the axes.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Describe the Hildreth-Lu Scanning procedure.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Describe the weighted least-squares procedure.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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[ui uj] = 0 for all i ≠\neq= j.

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

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Given Yi = β^0\hat { \beta } _ { 0 }β^​0​ + β^1\hat { \beta } _ { 1 }β^​1​ X1i + β^2\hat { \beta } _ { 2 }β^​2​ X2i + ei, describe the White test for heteroskedasticity making sure to specify the auxiliary regression in this case.

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.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -What do you recommend in a situation where GLS does not remedy autocorrelation? Explain.

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

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -Are the standard errors of the coefficients in a regression expected to increase or decrease after Newey-West is applied? Explain why?

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

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -What is generalized least-squares?

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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.

The Nature of Econometrics

Simple Regression Analysis

Residual Statistics

Hypothesis Testing

Alternate Functional Forms

Filters

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.

AVG =77 RANGE: 30-100 A - 4 B - C - 2 D - F - 2 -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.

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