Question 1

Which one of the following would be the most appropriate auxiliary regression for a Ramsey RESET test of functional form?

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)    A

Question 2

A normal distribution has coefficients of skewness and excess kurtosis which are respectively

Accepted Answer

A)  0 and 0 
B)  0 and 3 
C)  3 and 0 
D)  Will vary from one normal distribution to another 
A)  0 and 0 
B)  0 and 3 
C)  3 and 0 
D)  Will vary from one normal distribution to another A

Question 3

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

Accepted Answer

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

Question 4

Test statistics for the LM test and the Wald test are usually constructed to follow a

Accepted Answer

A)  χ2 distribution and F-distribution, respectively 
B)  χ2 distribution and t-distribution, respectively 
C)  F-distribution and χ2 distribution, respectively 
D)  t-distribution and χ2 distribution, respectively 
A)  χ2 distribution and F-distribution, respectively 
B)  χ2 distribution and t-distribution, respectively 
C)  F-distribution and χ2 distribution, respectively 
D)  t-distribution and χ2 distribution, respectively

Question 5

If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin Watson statistic?

Accepted Answer

A)  Close to zero 
B)  Close to two 
C)  Close to four 
D)  Close to one. 
A)  Close to zero 
B)  Close to two 
C)  Close to four 
D)  Close to one.

Question 6

Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?

Accepted Answer

A)  The assumption that the regressors are non-stochastic is violated 
B)  A model with many lags may lead to residual non-normality 
C)  Adding lags may induce multicollinearity with current values of variables 
D)  The standard errors of the coefficients will fall as a result of adding more explanatory variables 
A)  The assumption that the regressors are non-stochastic is violated 
B)  A model with many lags may lead to residual non-normality 
C)  Adding lags may induce multicollinearity with current values of variables 
D)  The standard errors of the coefficients will fall as a result of adding more explanatory variables

Question 7

Which of the following is NOT a good reason for including lagged variables in a regression?

Accepted Answer

A)  Slow response of the dependent variable to changes in the independent variables 
B)  Over-reactions of the dependent variables 
C)  The dependent variable is a centred moving average of the past 4 values of the series 
D)  The residuals of the model appear to be non-normal 
A)  Slow response of the dependent variable to changes in the independent variables 
B)  Over-reactions of the dependent variables 
C)  The dependent variable is a centred moving average of the past 4 values of the series 
D)  The residuals of the model appear to be non-normal

Question 8

Simultaneous equations bias is a situation where

Accepted Answer

A)  There is a two-way causal relationship between the explanatory and explained variable 
B)  There is a two-way causal relationship between two selected explanatory variables 
C)  There is a two-way causal relationship between two selected independent variables 
D)  There is a two-way causal relationship between the residuals of two regression models 
A)  There is a two-way causal relationship between the explanatory and explained variable 
B)  There is a two-way causal relationship between two selected explanatory variables 
C)  There is a two-way causal relationship between two selected independent variables 
D)  There is a two-way causal relationship between the residuals of two regression models

Question 9

If a regression equation contains an irrelevant variable, the parameter estimates will be

Accepted Answer

A)  Consistent and unbiased but inefficient 
B)  Consistent and asymptotically efficient but biased 
C)  Inconsistent 
D)  Consistent, unbiased and efficient. 
A)  Consistent and unbiased but inefficient 
B)  Consistent and asymptotically efficient but biased 
C)  Inconsistent 
D)  Consistent, unbiased and efficient.

Question 10

Which of these statements is true?
(I) The F-distribution has 2 degrees of freedom parameters
(II) Asymptotically, the LM test and the Wald test are equivalent
(III) The results from the LM and Wald tests may differ somewhat in small samples
(IV) The F-distribution is a special case of the t-distribution

Accepted Answer

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

Question 11

Which of the following conditions must be fulfilled for the Durbin Watson test to be valid?
(I) The regression includes a constant term
(ii) The regressors are non-stochastic
(iii) There are no lags of the dependent variable in the regression
(iv) There are no lags of the independent variables in the regression

Accepted Answer

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

Question 12

Which of the following would probably NOT be a potential "cure" for non-normal residuals?

Accepted Answer

A)  Transforming two explanatory variables into a ratio 
B)  Removing large positive residuals 
C)  Using a procedure for estimation and inference which did not assume normality 
D)  Removing large negative residuals 
A)  Transforming two explanatory variables into a ratio 
B)  Removing large positive residuals 
C)  Using a procedure for estimation and inference which did not assume normality 
D)  Removing large negative residuals

Question 13

What is the long run solution to the following dynamic econometric model?$\Delta$yt = $\beta$1 + $\beta$2$\Delta$X2t + $\beta$3$\Delta$X3t + ut

Accepted Answer

A)  y = $\beta$1 + $\beta$2X2 + $\beta$3X3 
B)  yt = $\beta$1 + $\beta$2X2t + $\beta$3X3t 
C)  y = - ($\beta$2/ $\beta$1) X2 - ($\beta$3 / $\beta$1)X3 
D)  There is no long run solution to this equation 
A)  y = $\beta$1 + $\beta$2X2 + $\beta$3X3 
B)  yt = $\beta$1 + $\beta$2X2t + $\beta$3X3t 
C)  y = - ($\beta$2/ $\beta$1) X2 - ($\beta$3 / $\beta$1)X3 
D)  There is no long run solution to this equation

Question 14

Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?

Accepted Answer

A)  Removing one of the explanatory variables 
B)  Transforming the data into logarithms 
C)  Transforming two of the explanatory variables into ratios 
D)  Collecting higher frequency data on all of the variables 
A)  Removing one of the explanatory variables 
B)  Transforming the data into logarithms 
C)  Transforming two of the explanatory variables into ratios 
D)  Collecting higher frequency data on all of the variables

Question 15

The assumption of homoscedasticity can be written mathematically as

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)

Question 16

A)  B)  
-The graphs above are time series plots of residuals from two separate regressions. Which of these combinations is true?

Accepted Answer

A)  A shows negative autocorrelation and B shows positive autocorrelation 
B)  A shows positive autocorrelation and B shows negative autocorrelation 
C)  A shows heteroscasticity and B shows homoscedasticity 
D)  A shows homoscedasticity and B shows heteroscasticity 
A)  A shows negative autocorrelation and B shows positive autocorrelation 
B)  A shows positive autocorrelation and B shows negative autocorrelation 
C)  A shows heteroscasticity and B shows homoscedasticity 
D)  A shows homoscedasticity and B shows heteroscasticity

Question 17

Assuming you are interested in conducting a Goldfeld-Quandt test at a 5% significance level and the regression model is estimated on each sub-sample with residual variances and , , and . What would your conclusion be?

Accepted Answer

A)  Do not reject the null hypothesis of heteroscedasticity 
B)  Reject the null hypothesis of homoscedasticity 
C)  Reject the null hypothesis of heteroscedasticity 
D)  Do not reject the null hypothesis of homoscedasticity 
A)  Do not reject the null hypothesis of heteroscedasticity 
B)  Reject the null hypothesis of homoscedasticity 
C)  Reject the null hypothesis of heteroscedasticity 
D)  Do not reject the null hypothesis of homoscedasticity

Question 18

Which of these is not a viable 'solution' for heteroscedasticity?

Accepted Answer

A)  Using generalised least squares ifr the form of heteroscedasticity is known 
B)  Transforming the variables into logs 
C)  Using heteroscedasticity-consistent standard error estimates 
D)  Taking the first differences of the series 
A)  Using generalised least squares ifr the form of heteroscedasticity is known 
B)  Transforming the variables into logs 
C)  Using heteroscedasticity-consistent standard error estimates 
D)  Taking the first differences of the series

Question 19

If the residuals of a regression on a large sample are found to be heteroscedastic which of the following might be a likely consequence?
(I) The coefficient estimates are biased
(ii) The standard error estimates for the slope coefficients may be too small
(iii) Statistical inferences may be wrong

Accepted Answer

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

Question 20

If the residuals of a model containing lags of the dependent variable are autocorrelated, which one of the following could this lead to?

Accepted Answer

A)  Biased but consistent coefficient estimates 
B)  Biased and inconsistent coefficient estimates 
C)  Unbiased but inconsistent coefficient estimates 
D)  Unbiased and consistent but inefficient coefficient estimates. 
A)  Biased but consistent coefficient estimates 
B)  Biased and inconsistent coefficient estimates 
C)  Unbiased but inconsistent coefficient estimates 
D)  Unbiased and consistent but inefficient coefficient estimates.

Which one of the following would be the most appropriate auxiliary regression for a Ramsey RESET test of functional form?

A normal distribution has coefficients of skewness and excess kurtosis which are respectively

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

Test statistics for the LM test and the Wald test are usually constructed to follow a

If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin Watson statistic?

Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?

Which of the following is NOT a good reason for including lagged variables in a regression?

Simultaneous equations bias is a situation where

If a regression equation contains an irrelevant variable, the parameter estimates will be

Which of the following would probably NOT be a potential "cure" for non-normal residuals?

What is the long run solution to the following dynamic econometric model? $\Delta$ y_t = $\beta$ ₁ + $\beta$ ₂ $\Delta$ X₂_t + $\beta$ ₃ $\Delta$ X₃_t + u_t

Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?

The assumption of homoscedasticity can be written mathematically as

A) B) -The graphs above are time series plots of residuals from two separate regressions. Which of these combinations is true?

Assuming you are interested in conducting a Goldfeld-Quandt test at a 5% significance level and the regression model is estimated on each sub-sample with residual variances and , , and . What would your conclusion be?

Which of these is not a viable 'solution' for heteroscedasticity?

If the residuals of a regression on a large sample are found to be heteroscedastic which of the following might be a likely consequence? (I) The coefficient estimates are biased (ii) The standard error estimates for the slope coefficients may be too small (iii) Statistical inferences may be wrong

If the residuals of a model containing lags of the dependent variable are autocorrelated, which one of the following could this lead to?

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 one of the following would be the most appropriate auxiliary regression for a Ramsey RESET test of functional form?

A normal distribution has coefficients of skewness and excess kurtosis which are respectively

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

Test statistics for the LM test and the Wald test are usually constructed to follow a

If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin Watson statistic?

Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?

Which of the following is NOT a good reason for including lagged variables in a regression?

Simultaneous equations bias is a situation where

If a regression equation contains an irrelevant variable, the parameter estimates will be

Which of the following would probably NOT be a potential "cure" for non-normal residuals?

What is the long run solution to the following dynamic econometric model? Δ\DeltaΔ yt = β\betaβ 1 + β\betaβ 2 Δ\DeltaΔ X2t + β\betaβ 3 Δ\DeltaΔ X3t + ut

Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?

The assumption of homoscedasticity can be written mathematically as

A) B) -The graphs above are time series plots of residuals from two separate regressions. Which of these combinations is true?

Assuming you are interested in conducting a Goldfeld-Quandt test at a 5% significance level and the regression model is estimated on each sub-sample with residual variances and , , and . What would your conclusion be?

Which of these is not a viable 'solution' for heteroscedasticity?

If the residuals of a regression on a large sample are found to be heteroscedastic which of the following might be a likely consequence? (I) The coefficient estimates are biased (ii) The standard error estimates for the slope coefficients may be too small (iii) Statistical inferences may be wrong

If the residuals of a model containing lags of the dependent variable are autocorrelated, which one of the following could this lead to?

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

What is the long run solution to the following dynamic econometric model? $\Delta$ y_t = $\beta$ ₁ + $\beta$ ₂ $\Delta$ X₂_t + $\beta$ ₃ $\Delta$ X₃_t + u_t