Question 1

The appropriate critical value for performing the Goldfeld-Quandt test at the 95% level is

Accepted Answer

A)  $F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , .025 }$ 
B)  $F _ { n _ { 1 } - k _ { 1 } , 05 }$ 
C)  $F _ { n _ { 2 } - k _ { 2 } , 05 }$ 
D)  $F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , 05 }$ 
A)  $F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , .025 }$ 
B)  $F _ { n _ { 1 } - k _ { 1 } , 05 }$ 
C)  $F _ { n _ { 2 } - k _ { 2 } , 05 }$ 
D)  $F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , 05 }$

Question 2

What is the intuition behind the Goldfeld-Quandt test for heteroskedasticity? Explain.

Accepted Answer

The intuition behind the Goldfeld-Quandt

Question 3

The final step in the Breusch-Pagan test is to perform

Accepted Answer

A) an F-test of the overall significance of the squared residuals regression. 
B) an F-test of the overall significance of the original OLS regression. 
C) a t-test of the overall significance of the squared residuals regression. 
D) an F-test of the individual significance of the suspect independent variable in the squared residuals regression. 
A) an F-test of the overall significance of the squared residuals regression. 
B) an F-test of the overall significance of the original OLS regression. 
C) a t-test of the overall significance of the squared residuals regression. 
D) an F-test of the individual significance of the suspect independent variable in the squared residuals regression.

Question 4

A simple way to generate some idea whether data are likely to be heteroskedastic is to

Accepted Answer

A) examine the residual plot. 
B) construct a histogram. 
C) calculate the variance of the sample. 
D) plot the data points from smallest to largest. 
A) examine the residual plot. 
B) construct a histogram. 
C) calculate the variance of the sample. 
D) plot the data points from smallest to largest.

Question 5

The second step in the Modified White's test is to regress the

Accepted Answer

A) residuals on the predicted value of the dependent variable. 
B) squared residuals on the predicted value of the dependent variable. 
C) squared residuals on the predicted value of the dependent variable squared. 
D) squared residuals on the predicted value of the dependent variable and the predicted value of the dependent variable squared. 
A) residuals on the predicted value of the dependent variable. 
B) squared residuals on the predicted value of the dependent variable. 
C) squared residuals on the predicted value of the dependent variable squared. 
D) squared residuals on the predicted value of the dependent variable and the predicted value of the dependent variable squared.

Question 6

Why are White's heteroskedastic consistent standard errors the preferred method for dealing with potential heteroskedasticity? Explain.

Accepted Answer

Because they are very,very eas

Question 7

Heteroskedasticity is a problem because it results in

Accepted Answer

A) biased parameter estimates. 
B) estimated standard errors that are incorrect. 
C) estimated standard errors that are always too small. 
D) incorrect estimated slope coefficients. 
A) biased parameter estimates. 
B) estimated standard errors that are incorrect. 
C) estimated standard errors that are always too small. 
D) incorrect estimated slope coefficients.

Question 8

The null hypothesis for testing for the presence of heteroskedasticity is

Accepted Answer

A) H0 : the error term has constant variance. 
B) H0 : the error term has non-constant variance. 
C) H0 : β1 = 0. 
D) H0 : Ɛ1 = 0. 
A) H0 : the error term has constant variance. 
B) H0 : the error term has non-constant variance. 
C) H0 : β1 = 0. 
D) H0 : Ɛ1 = 0.

Question 9

What is heteroskedasticity? Why is it problematic? Explain.

Accepted Answer

Heteroskedasticity occurs when the error

Question 10

The third step in the Goldfeld-Quandt test is to calculate the estimated sample regression function for the

Accepted Answer

A) bottom c observations in the ordered data set. 
B) middle c observations in the ordered data set. 
C) top c observations in the ordered data set. 
D) bottom cand the top c observations in the ordered data set. 
A) bottom c observations in the ordered data set. 
B) middle c observations in the ordered data set. 
C) top c observations in the ordered data set. 
D) bottom cand the top c observations in the ordered data set.

Question 11

The second step in the Goldfeld-Quandt test is to

Accepted Answer

A) omit the middle c observations from the ordered data set. 
B) omit the bottom c observations from the ordered data set. 
C) regress the squared residuals on the independent variables from the original OLS regression. 
D) regress the squared residuals on the predicted value of the dependent variable from the original OLS regression. 
A) omit the middle c observations from the ordered data set. 
B) omit the bottom c observations from the ordered data set. 
C) regress the squared residuals on the independent variables from the original OLS regression. 
D) regress the squared residuals on the predicted value of the dependent variable from the original OLS regression.

Question 12

How do you perform Weighted Least Squares? Why should it work? Explain.

Accepted Answer

You perform Weighted Least Squares by:(1

Question 13

Suppose you are interested in explaining variation in Body Mass Index in a nationally-representative sample of 12,486 men and women and that you estimate the sample regression function (standard errors in parentheses) $$\begin{array} { l } 
B M I _ { i } = &26.37 -& 5.61 \text { Female } _ { i } +& 0.48 \mathrm { Age } _ { i } \
& (4.62) &(1.05) &(0.13)  \
\end{array}$$ 
a)Which variable do you suspect could be responsible for heteroskedasticity in this model? Why? Explain.
b)How would you use the Breusch-Pagan test to determine whether heteroskedasticity is present? Explain.
c)How would you use Weighted Least Squares to account for heteroskedasticity if it is present? Explain.

Accepted Answer

a.It seems most reasonable that Age is l

Question 14

Heteroskedasticity occurs when

Accepted Answer

A) the error variance is constant. 
B) the error variance is non-constant. 
C) the dependent variable variance is constant. 
D) the dependent variable variance is non-constant. 
A) the error variance is constant. 
B) the error variance is non-constant. 
C) the dependent variable variance is constant. 
D) the dependent variable variance is non-constant.

Question 15

How do you perform the Breusch-Pagan test for heteroskedasticity? Explain.

Accepted Answer

You perform the Breusch-Pagan test by:(1

Question 16

The final step in the Goldfeld-Quandt test is to

Accepted Answer

A) perform an F-test of the overall significance of the estimated sample regression functions. 
B) calculate the Goldfeld-Quandt test statistic and compare it to the appropriate critical value. 
C) compare the calculated R2 values for the estimated sample regression functions. 
D) a t-test of the individual significance of the suspect independent variable in the squared residuals regression. 
A) perform an F-test of the overall significance of the estimated sample regression functions. 
B) calculate the Goldfeld-Quandt test statistic and compare it to the appropriate critical value. 
C) compare the calculated R2 values for the estimated sample regression functions. 
D) a t-test of the individual significance of the suspect independent variable in the squared residuals regression.

Question 17

White's Heteroskedastic standard errors are

Accepted Answer

A) the preferred method for correcting for potential heteroskedasticity. 
B) calculated through an iterative process. 
C) automatically calculated in Excel. 
D) the result of performing weighted least squares. 
A) the preferred method for correcting for potential heteroskedasticity. 
B) calculated through an iterative process. 
C) automatically calculated in Excel. 
D) the result of performing weighted least squares.

Question 18

The first step in the Breusch-Pagan test is to estimate the model and

Accepted Answer

A) calculate the predicted values of the dependent variable. 
B) calculate the estimated residuals. 
C) plot the estimated residuals. 
D) test whether the residuals are non-constant. 
A) calculate the predicted values of the dependent variable. 
B) calculate the estimated residuals. 
C) plot the estimated residuals. 
D) test whether the residuals are non-constant.

Question 19

What are the null and alternative hypothesis for testing for the presence of heteroskedasticity? Why? Explain.

Accepted Answer

The null hypothesis for testing for the

The appropriate critical value for performing the Goldfeld-Quandt test at the 95% level is

What is the intuition behind the Goldfeld-Quandt test for heteroskedasticity? Explain.

The final step in the Breusch-Pagan test is to perform

A simple way to generate some idea whether data are likely to be heteroskedastic is to

The second step in the Modified White's test is to regress the

Why are White's heteroskedastic consistent standard errors the preferred method for dealing with potential heteroskedasticity? Explain.

Heteroskedasticity is a problem because it results in

The null hypothesis for testing for the presence of heteroskedasticity is

What is heteroskedasticity? Why is it problematic? Explain.

The third step in the Goldfeld-Quandt test is to calculate the estimated sample regression function for the

The second step in the Goldfeld-Quandt test is to

How do you perform Weighted Least Squares? Why should it work? Explain.

Heteroskedasticity occurs when

How do you perform the Breusch-Pagan test for heteroskedasticity? Explain.

The final step in the Goldfeld-Quandt test is to

White's Heteroskedastic standard errors are

The first step in the Breusch-Pagan test is to estimate the model and

What are the null and alternative hypothesis for testing for the presence of heteroskedasticity? Why? Explain.

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Model Selection in Multiple Linear Regression Analysis

Time Series Analysis

Auto-Correlation

Limited Dependent Variables

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters

Exam 9: Heteroskedasticity

The appropriate critical value for performing the Goldfeld-Quandt test at the 95% level is

What is the intuition behind the Goldfeld-Quandt test for heteroskedasticity? Explain.

The final step in the Breusch-Pagan test is to perform

A simple way to generate some idea whether data are likely to be heteroskedastic is to

The second step in the Modified White's test is to regress the

Why are White's heteroskedastic consistent standard errors the preferred method for dealing with potential heteroskedasticity? Explain.

Heteroskedasticity is a problem because it results in

The null hypothesis for testing for the presence of heteroskedasticity is

What is heteroskedasticity? Why is it problematic? Explain.

The third step in the Goldfeld-Quandt test is to calculate the estimated sample regression function for the

The second step in the Goldfeld-Quandt test is to

How do you perform Weighted Least Squares? Why should it work? Explain.

Heteroskedasticity occurs when

How do you perform the Breusch-Pagan test for heteroskedasticity? Explain.

The final step in the Goldfeld-Quandt test is to

White's Heteroskedastic standard errors are

The first step in the Breusch-Pagan test is to estimate the model and

What are the null and alternative hypothesis for testing for the presence of heteroskedasticity? Why? Explain.

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Model Selection in Multiple Linear Regression Analysis

Time Series Analysis

Auto-Correlation

Limited Dependent Variables

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters