Question 1

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 2

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 3

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 4

Which of the following tests is used to compare the Ordinary Least Squares (OLS) estimates and the Weighted Least Squares (WLS) estimates?

Accepted Answer

A) The White test 
B) The Hausman test 
C)  The Durbin-Watson test 
D)  The Breusch-Godfrey test 
A) The White test 
B) The Hausman test 
C)  The Durbin-Watson test 
D)  The Breusch-Godfrey test

Question 5

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

Accepted Answer

A) True 
 B)False

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

Which of the following is true of the White test?

Which of the following is true?

Which of the following tests is used to compare the Ordinary Least Squares (OLS) estimates and the Weighted Least Squares (WLS) estimates?

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

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: Binary or Dummy Variables

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series 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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 8: Heteroskedasticity

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

Which of the following is true of the White test?

Which of the following is true?

Which of the following tests is used to compare the Ordinary Least Squares (OLS) estimates and the Weighted Least Squares (WLS) estimates?

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

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: Binary or Dummy Variables

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series 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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters