Question 1

Which of these is a test for heteroscedasticity?

Accepted Answer

A)  Breusch-Godfrey test 
B)  White test 
C)  Bera-Jarque test 
D)  Breusch-Jagan test 
A)  Breusch-Godfrey test 
B)  White test 
C)  Bera-Jarque test 
D)  Breusch-Jagan test

Question 2

The value of the Durbin Watson test statistic in a regression with 4 regressors (including the constant term) estimated on 100 observations is 3.6. What might we suggest from this?

Accepted Answer

A)  The residuals are positively autocorrelated 
B)  The residuals are negatively autocorrelated 
C)  There is no autocorrelation in the residuals 
D)  The test statistic has fallen in the intermediate region 
A)  The residuals are positively autocorrelated 
B)  The residuals are negatively autocorrelated 
C)  There is no autocorrelation in the residuals 
D)  The test statistic has fallen in the intermediate region

Question 3

Assuming the researcher now runs the following regression where is residual from a regression. If the researcher conducts a test with a null hypothesis of and and ... and against an alternative hypothesis of or or ... or , what type of test is he/she conducting?

Accepted Answer

A)  Test for rth order of heteroscedasticity 
B)  Test for rth order of autocorrelation 
C)  Test for rth order of non-normality 
D)  Test for rth order of homoscedasticity 
A)  Test for rth order of heteroscedasticity 
B)  Test for rth order of autocorrelation 
C)  Test for rth order of non-normality 
D)  Test for rth order of homoscedasticity

Question 4

If OLS is used in the presence of heteroscedasticity, which of the following will be likely consequences?
(I) Coefficient estimates may be misleading
(ii) Hypothesis tests could reach the wrong conclusions
(iii) Forecasts made from the model could be biased
(iv) Standard errors may inappropriate

Accepted Answer

A)  (ii) and (iv) only 
B)  (i) and (iii) only 
C)  (i), (ii), and (iii) only 
D)  (i), (ii), (iii), and (iv). 
A)  (ii) and (iv) only 
B)  (i) and (iii) only 
C)  (i), (ii), and (iii) only 
D)  (i), (ii), (iii), and (iv).

Question 5

Which of these is a viable solution to the problem of multicollinearity?
(I) Ignore it
(II) Drop one of the collinear variables
(III) Transform the highly correlated variables into a ratio
(IV) Take the logs of the variables

Accepted Answer

A)  I only 
B)  I and II only 
C)  I, II and III only 
D)  I, II, III and IV 
A)  I only 
B)  I and II only 
C)  I, II and III only 
D)  I, II, III and IV

Question 6

Which of these is not a consequence of ignoring autocorrelation if it is present?

Accepted Answer

A)  The coefficient estimates derived using OLS are inefficient 
B)  Standard error estimates are inappropriate 
C)  The coefficient estimates derived using OLS are biased 
D)  The coefficient estimates derived using OLS are not the best linear unbiased estimators 
A)  The coefficient estimates derived using OLS are inefficient 
B)  Standard error estimates are inappropriate 
C)  The coefficient estimates derived using OLS are biased 
D)  The coefficient estimates derived using OLS are not the best linear unbiased estimators

Question 7

Which of the following statements are true about parameter stability tests?
(I) Parameter stability tests test the assumption that the estimated parameters of a model are constant for the entire sample
(II) Chow test and predictive failure tests are two types of parameter stability tests
(III) Backward and forward predictive failure tests are two types of parameter stability tests
(IV) Parameter stability tests examine violations of the classical linear regression model assumptions

Accepted Answer

A)  I only 
B)  I and II only 
C)  I, II and III only 
D)  I, II, III and IV 
A)  I only 
B)  I and II only 
C)  I, II and III only 
D)  I, II, III and IV

Question 8

Put the following steps of the model-building process in the order in which it would be statistically most appropriate to do them:
(I) Estimate model
(ii) Conduct hypothesis tests on coefficients
(iii) Remove irrelevant variables
(iv) Conduct diagnostic tests on the model residuals

Accepted Answer

A)  (i) then (ii) then (iii) then (iv) 
B)  (i) then (iv) then (ii) then (iii) 
C)  (i) then (iv) then (iii) then (ii) 
D)  (i) then (iii) then (ii) then (iv). 
A)  (i) then (ii) then (iii) then (iv) 
B)  (i) then (iv) then (ii) then (iii) 
C)  (i) then (iv) then (iii) then (ii) 
D)  (i) then (iii) then (ii) then (iv).

Question 9

Which one of the following is NOT a symptom of near multicollinearity?

Accepted Answer

A)  The R2 value is high 
B)  The regression results change substantively when one particular variable is deleted 
C)  Confidence intervals on parameter estimates are narrow 
D)  Individual parameter estimates are insignificant 
A)  The R2 value is high 
B)  The regression results change substantively when one particular variable is deleted 
C)  Confidence intervals on parameter estimates are narrow 
D)  Individual parameter estimates are insignificant

Which of these is a test for heteroscedasticity?

The value of the Durbin Watson test statistic in a regression with 4 regressors (including the constant term) estimated on 100 observations is 3.6. What might we suggest from this?

Assuming the researcher now runs the following regression where is residual from a regression. If the researcher conducts a test with a null hypothesis of and and ... and against an alternative hypothesis of or or ... or , what type of test is he/she conducting?

If OLS is used in the presence of heteroscedasticity, which of the following will be likely consequences? (I) Coefficient estimates may be misleading (ii) Hypothesis tests could reach the wrong conclusions (iii) Forecasts made from the model could be biased (iv) Standard errors may inappropriate

Which of these is a viable solution to the problem of multicollinearity? (I) Ignore it (II) Drop one of the collinear variables (III) Transform the highly correlated variables into a ratio (IV) Take the logs of the variables

Which of these is not a consequence of ignoring autocorrelation if it is present?

Put the following steps of the model-building process in the order in which it would be statistically most appropriate to do them: (I) Estimate model (ii) Conduct hypothesis tests on coefficients (iii) Remove irrelevant variables (iv) Conduct diagnostic tests on the model residuals

Which one of the following is NOT a symptom of near multicollinearity?

Introduction

Mathematical and Statistical Foundations

A Brief Overview of the Classical Linear Regression Model

Further Development and Analysis of the Classical Linear Regression Model

Classical Linear Regression Model Assumptions and Diagnostic Tests

Multivariate Models

Modelling Long-Run Relationships in Finance

Modelling Volatility and Correlation

Switching Models

Panel Data and Limited Dependent Variable Models

Filters

Exam 6: Univariate Time Series Modelling and Forecasting

Which of these is a test for heteroscedasticity?

The value of the Durbin Watson test statistic in a regression with 4 regressors (including the constant term) estimated on 100 observations is 3.6. What might we suggest from this?

Assuming the researcher now runs the following regression where is residual from a regression. If the researcher conducts a test with a null hypothesis of and and ... and against an alternative hypothesis of or or ... or , what type of test is he/she conducting?

If OLS is used in the presence of heteroscedasticity, which of the following will be likely consequences? (I) Coefficient estimates may be misleading (ii) Hypothesis tests could reach the wrong conclusions (iii) Forecasts made from the model could be biased (iv) Standard errors may inappropriate

Which of these is a viable solution to the problem of multicollinearity? (I) Ignore it (II) Drop one of the collinear variables (III) Transform the highly correlated variables into a ratio (IV) Take the logs of the variables

Which of these is not a consequence of ignoring autocorrelation if it is present?

Put the following steps of the model-building process in the order in which it would be statistically most appropriate to do them: (I) Estimate model (ii) Conduct hypothesis tests on coefficients (iii) Remove irrelevant variables (iv) Conduct diagnostic tests on the model residuals

Which one of the following is NOT a symptom of near multicollinearity?

Introduction

Mathematical and Statistical Foundations

A Brief Overview of the Classical Linear Regression Model

Further Development and Analysis of the Classical Linear Regression Model

Classical Linear Regression Model Assumptions and Diagnostic Tests

Multivariate Models

Modelling Long-Run Relationships in Finance

Modelling Volatility and Correlation

Switching Models

Panel Data and Limited Dependent Variable Models

Filters