Chapter 8

Which of the following is true of heteroskedasticity?

• Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators.

• Population R2 is affected by the presence of heteroskedasticty.

• The Ordinary Least Square estimators are not the best linear unbiased estimators if heteroskedasticity is present.

• It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form.

Consider the following regression model: yi=β0+β1 xi+ui. If the first four Gauss-Markov assumptions hold true, and the error term contains heteroskedasticity, then _____.

• Var(ui|xi) =0

• Var(ui|xi) =1

• Var(ui|xi) = σi^2

• Var(ui|xi) =σ

The general form of the t statistic is _____.

• t= (estimate−hypothesized value)/ standard error

• t= (hypothesized value−estimate)/ standard error

• t= standard error (estimate−hypothesized value)

• t=estimate−hypothesized value

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

• The heteroskedasticity-robust t statistics are justified only if the sample size is large.

• The heteroskedasticty-robust t statistics are justified only if the sample size is small.

• The usual t statistics do not have exact t distributions if the sample size is large.

• In the presence of homoscedasticity, the usual t statistics do not have exact t distributions if the sample size is small.

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

• t statistic

• F statistic

• LM statistic

• z statistic

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

• The Breusch-Pagan test

• The Breusch-Godfrey test

• The Durbin-Watson test

• The Chow test

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

• The model contains homoskedasticty.

• The model contains heteroskedasticty.

• The model contains dummy variables.

• The model omits some important explanatory factors.

A test for heteroskedasticty can be significant if _____.

• the Breusch-Pagan test results in a large p-value

• the White test results in a large p-value

• the functional form of the regression model is misspecified

• the regression model includes too many independent variables

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

• The White test is used for detecting heteroskedasticty in a linear regression model while the Breusch-Pagan test is used for detecting autocorrelation.

• The White test is used for detecting autocorrelation in a linear regression model while the Breusch-Pagan test is used for detecting heteroskedasticity.

• The number of regressors used in the White test is larger than the number of regressors used in the Breusch-Pagan test.

• The number of regressors used in the Breusch-Pagan test is larger than the number of regressors used in the White test.

Which of the following is true of the White test?

• The White test is used to detect the presence of multicollinearity in a linear regression model.

• The White test cannot detect forms of heteroskedasticity that invalidate the usual Ordinary Least Squares standard errors.

• The White test can detect the presence of heteroskedasticty in a linear regression model even if the functional form is misspecified.

• 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.