Question 1

The heteroskedasticity-robust _____ is also called the heteroskedastcity-robust Wald statistic.

Accepted Answer

A) t statistic 
B) F statistic 
C) LM statistic 
D) z statistic 
A) t statistic 
B) F statistic 
C) LM statistic 
D) z statistic B

Question 2

The interpretation of goodness-of-fit measures changes in the presence of heteroskedasticity.

Accepted Answer

A) True 
 B)False  False

Question 3

When the error variance differs across the two groups, we can obtain a heteroskedasticity-robust Chow test by including a dummy variable distinguishing the two groups along with interactions between that dummy variable and all other explanatory variables.​

Accepted Answer

A) True 
 B)False  True

Question 4

The linear probability model always contains heteroskedasticity when the dependent variable is a binary variable unless all of the slope parameters are zero.

Accepted Answer

A) True 
 B)False

Question 5

What will you conclude about a regression model if the Breusch-Pagan test results in a small p-value?

Accepted Answer

A) The model contains homoskedasticty. 
B) The model contains heteroskedasticty. 
C) The model contains dummy variables. 
D) The model omits some important explanatory factors. 
A) The model contains homoskedasticty. 
B) The model contains heteroskedasticty. 
C) The model contains dummy variables. 
D) The model omits some important explanatory factors.

Question 6

Which of the following is true of the OLS t statistics?

Accepted Answer

A) The heteroskedasticity-robust t statistics are justified only if the sample size is large. 
B) The heteroskedasticty-robust t statistics are justified only if the sample size is small. 
C) The usual t statistics do not have exact t distributions if the sample size is large. 
D) In the presence of homoskedasticity, the usual t statistics do not have exact t distributions if the sample size is small. 
A) The heteroskedasticity-robust t statistics are justified only if the sample size is large. 
B) The heteroskedasticty-robust t statistics are justified only if the sample size is small. 
C) The usual t statistics do not have exact t distributions if the sample size is large. 
D) In the presence of homoskedasticity, the usual t statistics do not have exact t distributions if the sample size is small.

Question 7

Which of the following is true of the White test?

Accepted Answer

A) The White test is used to detect the presence of multicollinearity in a linear regression model. 
B) The White test cannot detect forms of heteroskedasticity that invalidate the usual Ordinary Least Squares standard errors. 
C) The White test can detect the presence of heteroskedasticty in a linear regression model even if the functional form is misspecified. 
D) The White test assumes that the square of the error term in a regression model is uncorrelated with all the independent variables, their squares and cross products. 
A) The White test is used to detect the presence of multicollinearity in a linear regression model. 
B) The White test cannot detect forms of heteroskedasticity that invalidate the usual Ordinary Least Squares standard errors. 
C) The White test can detect the presence of heteroskedasticty in a linear regression model even if the functional form is misspecified. 
D) The White test assumes that the square of the error term in a regression model is uncorrelated with all the independent variables, their squares and cross products.

Question 8

Which of the following is a difference between the White test and the Breusch-Pagan test?

Accepted Answer

A) The White test is used for detecting heteroskedasticty in a linear regression model while the Breusch-Pagan test is used for detecting autocorrelation. 
B) The White test is used for detecting autocorrelation in a linear regression model while the Breusch-Pagan test is used for detecting heteroskedasticity.. 
C) The number of regressors used in the White test is larger than the number of regressors used in the Breusch-Pagan test. 
D) The number of regressors used in the Breusch-Pagan test is larger than the number of regressors used in the White test. 
A) The White test is used for detecting heteroskedasticty in a linear regression model while the Breusch-Pagan test is used for detecting autocorrelation. 
B) The White test is used for detecting autocorrelation in a linear regression model while the Breusch-Pagan test is used for detecting heteroskedasticity.. 
C) The number of regressors used in the White test is larger than the number of regressors used in the Breusch-Pagan test. 
D) The number of regressors used in the Breusch-Pagan test is larger than the number of regressors used in the White test.

Question 9

The square root of the quantity   ​is called the _____ for   .

Accepted Answer

A) heteroskedasticity-robust t statistic​ 
B) ​heteroskedasticity-robust standard error 
C) ​heteroskedasticity-robust F statistic 
D) ​heteroskedasticity-robust Wald statistic 
A) heteroskedasticity-robust t statistic​ 
B) ​heteroskedasticity-robust standard error 
C) ​heteroskedasticity-robust F statistic 
D) ​heteroskedasticity-robust Wald statistic

Question 10

If the Breusch-Pagan Test for heteroskedasticity results in a large p-value, the null hypothesis of homoskedasticty is rejected.

Accepted Answer

A) True 
 B)False

Question 11

The generalized least square estimators for correcting heteroskedasticity are called weighed least squares estimators.

Accepted Answer

A) True 
 B)False

Question 12

Which of the following tests helps in the detection of heteroskedasticity?

Accepted Answer

A) The Breusch-Pagan test 
B) The Breusch-Godfrey test 
C) The Durbin-Watson test 
D) The Chow test 
A) The Breusch-Pagan test 
B) The Breusch-Godfrey test 
C) The Durbin-Watson test 
D) The Chow test

Question 13

A test for heteroskedasticty can be significant if _____.​

Accepted Answer

A) ​the Breusch-Pagan test results in a large p-value 
B) ​the White test results in a large p-value 
C) ​the functional form of the regression model is misspecified 
D) the regression model includes too many independent variables 
A) ​the Breusch-Pagan test results in a large p-value 
B) ​the White test results in a large p-value 
C) ​the functional form of the regression model is misspecified 
D) the regression model includes too many independent variables

Question 14

Which of the following is true?

Accepted Answer

A) In ordinary least squares estimation, each observation is given a different weight. 
B) In weighted least squares estimation, each observation is given an identical weight. 
C) In weighted least squares estimation, less weight is given to observations with a higher error variance. 
D) In ordinary least squares estimation, less weight is given to observations with a lower error variance. 
A) In ordinary least squares estimation, each observation is given a different weight. 
B) In weighted least squares estimation, each observation is given an identical weight. 
C) In weighted least squares estimation, less weight is given to observations with a higher error variance. 
D) In ordinary least squares estimation, less weight is given to observations with a lower error variance.

Question 15

The population R-squared is affected when heteroskedasticity is present in Var(u|x1, …, xk).​

Accepted Answer

A) True 
 B)False

Question 16

Which term(s) in the general form of the t statistic is (are) computed differently between the usual OLS t-statistic and the heteroskedasticity-robust t-statistic?

Accepted Answer

A) Estimate and standard error. 
B) Estimate, standard error, and the hypothesized value. 
C) Estimate only. 
D) Standard error only. 
A) Estimate and standard error. 
B) Estimate, standard error, and the hypothesized value. 
C) Estimate only. 
D) Standard error only.

Question 17

Which of the following is true of heteroskedasticity?

Accepted Answer

A) Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators. 
B) Population R2 is affected by the presence of heteroskedasticty. 
C) The Ordinary Least Square estimators are not the best linear unbiased estimators if heteroskedasticity is present. 
D) It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form. 
A) Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators. 
B) Population R2 is affected by the presence of heteroskedasticty. 
C) The Ordinary Least Square estimators are not the best linear unbiased estimators if heteroskedasticity is present. 
D) It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form.

Question 18

Which of the following is true?​

Accepted Answer

A) ​If we can estimate hi for each i, it means that we can proceed directly with WLS estimation. 
B) ​The WLS method fails if   i is negative or zero for any observation. 
C) ​The simplest way to deal with homoskedasticity in the linear probability model is to continue to use OLS estimation. 
D) ​The probability p(x) depends on the error term. 
A) ​If we can estimate hi for each i, it means that we can proceed directly with WLS estimation. 
B) ​The WLS method fails if   i is negative or zero for any observation. 
C) ​The simplest way to deal with homoskedasticity in the linear probability model is to continue to use OLS estimation. 
D) ​The probability p(x) depends on the error term.

Question 19

The generalized least square (GLS) is an efficient procedure that weights each squared residual by the:​

Accepted Answer

A) ​conditional variance of ui given xi. 
B) ​expected value of ui given xi. 
C) ​inverse of the conditional variance of ui given xi. 
D) ​square root of the inverse of the conditional variance of ui given xi. 
A) ​conditional variance of ui given xi. 
B) ​expected value of ui given xi. 
C) ​inverse of the conditional variance of ui given xi. 
D) ​square root of the inverse of the conditional variance of ui given xi.

Question 20

Whenever there is strong heteroskedasticity, it is preferable to use OLS rather than WLS, which may use a possibly misspecified variance function

Accepted Answer

A) True 
 B)False

The heteroskedasticity-robust _____ is also called the heteroskedastcity-robust Wald statistic.

The interpretation of goodness-of-fit measures changes in the presence of heteroskedasticity.

When the error variance differs across the two groups, we can obtain a heteroskedasticity-robust Chow test by including a dummy variable distinguishing the two groups along with interactions between that dummy variable and all other explanatory variables.

The linear probability model always contains heteroskedasticity when the dependent variable is a binary variable unless all of the slope parameters are zero.

What will you conclude about a regression model if the Breusch-Pagan test results in a small p-value?

Which of the following is true of the OLS t statistics?

Which of the following is true of the White test?

Which of the following is a difference between the White test and the Breusch-Pagan test?

The square root of the quantity is called the _____ for .

If the Breusch-Pagan Test for heteroskedasticity results in a large p-value, the null hypothesis of homoskedasticty is rejected.

The generalized least square estimators for correcting heteroskedasticity are called weighed least squares estimators.

Which of the following tests helps in the detection of heteroskedasticity?

A test for heteroskedasticty can be significant if _____.

Which of the following is true?

The population R-squared is affected when heteroskedasticity is present in Var(u|x₁, …, x_k).

Which term(s) in the general form of the t statistic is (are) computed differently between the usual OLS t-statistic and the heteroskedasticity-robust t-statistic?

Which of the following is true of heteroskedasticity?

Which of the following is true?

The generalized least square (GLS) is an efficient procedure that weights each squared residual by the:

Whenever there is strong heteroskedasticity, it is preferable to use OLS rather than WLS, which may use a possibly misspecified variance function

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Inference

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

More on Specification and Data Issues

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 8: Heteroskedasticity

The heteroskedasticity-robust _____ is also called the heteroskedastcity-robust Wald statistic.

The interpretation of goodness-of-fit measures changes in the presence of heteroskedasticity.

When the error variance differs across the two groups, we can obtain a heteroskedasticity-robust Chow test by including a dummy variable distinguishing the two groups along with interactions between that dummy variable and all other explanatory variables.​

The linear probability model always contains heteroskedasticity when the dependent variable is a binary variable unless all of the slope parameters are zero.

What will you conclude about a regression model if the Breusch-Pagan test results in a small p-value?

Which of the following is true of the OLS t statistics?

Which of the following is true of the White test?

Which of the following is a difference between the White test and the Breusch-Pagan test?

The square root of the quantity ​is called the _____ for .

If the Breusch-Pagan Test for heteroskedasticity results in a large p-value, the null hypothesis of homoskedasticty is rejected.

The generalized least square estimators for correcting heteroskedasticity are called weighed least squares estimators.

Which of the following tests helps in the detection of heteroskedasticity?

A test for heteroskedasticty can be significant if _____.​

Which of the following is true?

The population R-squared is affected when heteroskedasticity is present in Var(u|x1, …, xk).​

Which term(s) in the general form of the t statistic is (are) computed differently between the usual OLS t-statistic and the heteroskedasticity-robust t-statistic?

Which of the following is true of heteroskedasticity?

Which of the following is true?​

The generalized least square (GLS) is an efficient procedure that weights each squared residual by the:​

Whenever there is strong heteroskedasticity, it is preferable to use OLS rather than WLS, which may use a possibly misspecified variance function

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Inference

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

More on Specification and Data Issues

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

When the error variance differs across the two groups, we can obtain a heteroskedasticity-robust Chow test by including a dummy variable distinguishing the two groups along with interactions between that dummy variable and all other explanatory variables.

The square root of the quantity is called the _____ for .

A test for heteroskedasticty can be significant if _____.

The population R-squared is affected when heteroskedasticity is present in Var(u|x₁, …, x_k).

Which of the following is true?

The generalized least square (GLS) is an efficient procedure that weights each squared residual by the: