[["Question: A white noise process will have\n\n(i) A zero mean\n\n(ii) A constant variance\n\n(iii) Autocovariances that are constant\n\n(iv) Autocovariances that are zero except at lag zero\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (ii) and (iv) only"], ["Question: A white noise process will have\n\n(i) A zero mean\n\n(ii) A constant variance\n\n(iii) Autocovariances that are constant\n\n(iv) Autocovariances that are zero except at lag zero\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i) and (iii) only"], ["Question: A white noise process will have\n\n(i) A zero mean\n\n(ii) A constant variance\n\n(iii) Autocovariances that are constant\n\n(iv) Autocovariances that are zero except at lag zero\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i), (ii), and (iii) only"], ["Question: A white noise process will have\n\n(i) A zero mean\n\n(ii) A constant variance\n\n(iii) Autocovariances that are constant\n\n(iv) Autocovariances that are zero except at lag zero\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i), (ii), (iii), and (iv)"], ["Question: How many parameters will be required to be estimated in total for all equations of a standard form, unrestricted, tri-variate VAR(4), ignoring the intercepts?\nChoices:\nA. 12\nB. 4\nC. 3\nD. 36\nAnswer:", " 12"], ["Question: How many parameters will be required to be estimated in total for all equations of a standard form, unrestricted, tri-variate VAR(4), ignoring the intercepts?\nChoices:\nA. 12\nB. 4\nC. 3\nD. 36\nAnswer:", " 4"], ["Question: How many parameters will be required to be estimated in total for all equations of a standard form, unrestricted, tri-variate VAR(4), ignoring the intercepts?\nChoices:\nA. 12\nB. 4\nC. 3\nD. 36\nAnswer:", " 3"], ["Question: How many parameters will be required to be estimated in total for all equations of a standard form, unrestricted, tri-variate VAR(4), ignoring the intercepts?\nChoices:\nA. 12\nB. 4\nC. 3\nD. 36\nAnswer:", " 36"], ["Question: Which of the following are plausible approaches to dealing with residual autocorrelation?\n\ni) Take logarithms of each of the variables\n\nii) Add lagged values of the variables to the regression equation\n\niii) Use dummy variables to remove outlying observations\n\niv) Try a model in first differenced form rather than in levels.\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (ii) and (iv) only"], ["Question: Which of the following are plausible approaches to dealing with residual autocorrelation?\n\ni) Take logarithms of each of the variables\n\nii) Add lagged values of the variables to the regression equation\n\niii) Use dummy variables to remove outlying observations\n\niv) Try a model in first differenced form rather than in levels.\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i) and (iii) only"], ["Question: Which of the following are plausible approaches to dealing with residual autocorrelation?\n\ni) Take logarithms of each of the variables\n\nii) Add lagged values of the variables to the regression equation\n\niii) Use dummy variables to remove outlying observations\n\niv) Try a model in first differenced form rather than in levels.\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i), (ii), and (iii) only"], ["Question: Which of the following are plausible approaches to dealing with residual autocorrelation?\n\ni) Take logarithms of each of the variables\n\nii) Add lagged values of the variables to the regression equation\n\niii) Use dummy variables to remove outlying observations\n\niv) Try a model in first differenced form rather than in levels.\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i), (ii), (iii), and (iv)"], ["Question: Which one of the following is NOT an example of mis-specification of functional form?\nChoices:\nA. Using a linear specification when y scales as a function of the squares of x\nB. Using a linear specification when a double-logarithmic model would be more appropriate\nC. Modelling y as a function of x when in fact it scales as a function of 1/x\nD. Excluding a relevant variable from a linear regression model\nAnswer:", " Using a linear specification when y scales as a function of the squares of x"], ["Question: Which one of the following is NOT an example of mis-specification of functional form?\nChoices:\nA. Using a linear specification when y scales as a function of the squares of x\nB. Using a linear specification when a double-logarithmic model would be more appropriate\nC. Modelling y as a function of x when in fact it scales as a function of 1/x\nD. Excluding a relevant variable from a linear regression model\nAnswer:", " Using a linear specification when a double-logarithmic model would be more appropriate"], ["Question: Which one of the following is NOT an example of mis-specification of functional form?\nChoices:\nA. Using a linear specification when y scales as a function of the squares of x\nB. Using a linear specification when a double-logarithmic model would be more appropriate\nC. Modelling y as a function of x when in fact it scales as a function of 1/x\nD. Excluding a relevant variable from a linear regression model\nAnswer:", " Modelling y as a function of x when in fact it scales as a function of 1/x"], ["Question: Which one of the following is NOT an example of mis-specification of functional form?\nChoices:\nA. Using a linear specification when y scales as a function of the squares of x\nB. Using a linear specification when a double-logarithmic model would be more appropriate\nC. Modelling y as a function of x when in fact it scales as a function of 1/x\nD. Excluding a relevant variable from a linear regression model\nAnswer:", " Excluding a relevant variable from a linear regression model"], ["Question: The order condition is\nChoices:\nA. A necessary and sufficient condition for identification\nB. A necessary but not sufficient condition for identification\nC. A sufficient but not necessary condition for identification\nD. A condition that is nether necessary nor sufficient for identification\nAnswer:", " A necessary and sufficient condition for identification"], ["Question: The order condition is\nChoices:\nA. A necessary and sufficient condition for identification\nB. A necessary but not sufficient condition for identification\nC. A sufficient but not necessary condition for identification\nD. A condition that is nether necessary nor sufficient for identification\nAnswer:", " A necessary but not sufficient condition for identification"], ["Question: The order condition is\nChoices:\nA. A necessary and sufficient condition for identification\nB. A necessary but not sufficient condition for identification\nC. A sufficient but not necessary condition for identification\nD. A condition that is nether necessary nor sufficient for identification\nAnswer:", " A sufficient but not necessary condition for identification"], ["Question: The order condition is\nChoices:\nA. A necessary and sufficient condition for identification\nB. A necessary but not sufficient condition for identification\nC. A sufficient but not necessary condition for identification\nD. A condition that is nether necessary nor sufficient for identification\nAnswer:", " A condition that is nether necessary nor sufficient for identification"], ["Question: Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?\nChoices:\nA. The model is likely to be technical to estimate\nB. The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables\nC. The number of parameters to estimate may be large, resulting in a loss of degrees of freedom\nD. The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable.\nAnswer:", " The model is likely to be technical to estimate"], ["Question: Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?\nChoices:\nA. The model is likely to be technical to estimate\nB. The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables\nC. The number of parameters to estimate may be large, resulting in a loss of degrees of freedom\nD. The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable.\nAnswer:", " The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables"], ["Question: Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?\nChoices:\nA. The model is likely to be technical to estimate\nB. The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables\nC. The number of parameters to estimate may be large, resulting in a loss of degrees of freedom\nD. The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable.\nAnswer:", " The number of parameters to estimate may be large, resulting in a loss of degrees of freedom"], ["Question: Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?\nChoices:\nA. The model is likely to be technical to estimate\nB. The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables\nC. The number of parameters to estimate may be large, resulting in a loss of degrees of freedom\nD. The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable.\nAnswer:", " The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable."], ["Question: Which of the following statements are true concerning the standardised residuals (residuals divided by their respective conditional standard deviations) from an estimated GARCH model?\n\ni) They are assumed to be normally distributed\n\n\nii) Their squares will be related to their lagged squared values if the GARCH model is\n\nappropriate\n\n\niii) In practice, they are likely to have fat tails\n\n\niv) If the GARCH model is adequate, the standardised residuals and the raw residuals\n\nwill be identical\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (ii) and (iv) only"], ["Question: Which of the following statements are true concerning the standardised residuals (residuals divided by their respective conditional standard deviations) from an estimated GARCH model?\n\ni) They are assumed to be normally distributed\n\n\nii) Their squares will be related to their lagged squared values if the GARCH model is\n\nappropriate\n\n\niii) In practice, they are likely to have fat tails\n\n\niv) If the GARCH model is adequate, the standardised residuals and the raw residuals\n\nwill be identical\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i) and (iii) only"], ["Question: Which of the following statements are true concerning the standardised residuals (residuals divided by their respective conditional standard deviations) from an estimated GARCH model?\n\ni) They are assumed to be normally distributed\n\n\nii) Their squares will be related to their lagged squared values if the GARCH model is\n\nappropriate\n\n\niii) In practice, they are likely to have fat tails\n\n\niv) If the GARCH model is adequate, the standardised residuals and the raw residuals\n\nwill be identical\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i), (ii), and (iii) only"], ["Question: Which of the following statements are true concerning the standardised residuals (residuals divided by their respective conditional standard deviations) from an estimated GARCH model?\n\ni) They are assumed to be normally distributed\n\n\nii) Their squares will be related to their lagged squared values if the GARCH model is\n\nappropriate\n\n\niii) In practice, they are likely to have fat tails\n\n\niv) If the GARCH model is adequate, the standardised residuals and the raw residuals\n\nwill be identical\nChoices:\nA. (ii) and (iv) only\nB. (i) and (iii) only\nC. (i), (ii), and (iii) only\nD. (i), (ii), (iii), and (iv)\nAnswer:", " (i), (ii), (iii), and (iv)"], ["Question: Suppose that the Durbin Watson test is applied to a regression containing two explanatory variables plus a constant with 50 data points. The test statistic takes a value of 1.53. What is the appropriate conclusion?\nChoices:\nA. Residuals appear to be positively autocorrelated\nB. Residuals appear to be negatively autocorrelated\nC. Residuals appear not to be autocorrelated\nD. The test result is inconclusive\nAnswer:", " Residuals appear to be positively autocorrelated"], ["Question: Suppose that the Durbin Watson test is applied to a regression containing two explanatory variables plus a constant with 50 data points. The test statistic takes a value of 1.53. What is the appropriate conclusion?\nChoices:\nA. Residuals appear to be positively autocorrelated\nB. Residuals appear to be negatively autocorrelated\nC. Residuals appear not to be autocorrelated\nD. The test result is inconclusive\nAnswer:", " Residuals appear to be negatively autocorrelated"], ["Question: Suppose that the Durbin Watson test is applied to a regression containing two explanatory variables plus a constant with 50 data points. The test statistic takes a value of 1.53. What is the appropriate conclusion?\nChoices:\nA. Residuals appear to be positively autocorrelated\nB. Residuals appear to be negatively autocorrelated\nC. Residuals appear not to be autocorrelated\nD. The test result is inconclusive\nAnswer:", " Residuals appear not to be autocorrelated"], ["Question: Suppose that the Durbin Watson test is applied to a regression containing two explanatory variables plus a constant with 50 data points. The test statistic takes a value of 1.53. What is the appropriate conclusion?\nChoices:\nA. Residuals appear to be positively autocorrelated\nB. Residuals appear to be negatively autocorrelated\nC. Residuals appear not to be autocorrelated\nD. The test result is inconclusive\nAnswer:", " The test result is inconclusive"], ["Question: Suppose that we wished to evaluate the factors that affected the probability that an investor would choose an equity fund rather than a bond fund or a cash investment. Which class of model would be most appropriate?\nChoices:\nA. A logit model\nB. A multinomial logit\nC. A tobit model\nD. An ordered logit model\nAnswer:", " A logit model"], ["Question: Suppose that we wished to evaluate the factors that affected the probability that an investor would choose an equity fund rather than a bond fund or a cash investment. Which class of model would be most appropriate?\nChoices:\nA. A logit model\nB. A multinomial logit\nC. A tobit model\nD. An ordered logit model\nAnswer:", " A multinomial logit"], ["Question: Suppose that we wished to evaluate the factors that affected the probability that an investor would choose an equity fund rather than a bond fund or a cash investment. Which class of model would be most appropriate?\nChoices:\nA. A logit model\nB. A multinomial logit\nC. A tobit model\nD. An ordered logit model\nAnswer:", " A tobit model"], ["Question: Suppose that we wished to evaluate the factors that affected the probability that an investor would choose an equity fund rather than a bond fund or a cash investment. Which class of model would be most appropriate?\nChoices:\nA. A logit model\nB. A multinomial logit\nC. A tobit model\nD. An ordered logit model\nAnswer:", " An ordered logit model"], ["Question: Which of the following statements is TRUE concerning OLS estimation?\nChoices:\nA. OLS minimises the sum of the vertical distances from the points to the line\nB. OLS minimises the sum of the squares of the vertical distances from the points to the line\nC. OLS minimises the sum of the horizontal distances from the points to the line\nD. OLS minimises the sum of the squares of the horizontal distances from the points to the line.\nAnswer:", " OLS minimises the sum of the vertical distances from the points to the line"], ["Question: Which of the following statements is TRUE concerning OLS estimation?\nChoices:\nA. OLS minimises the sum of the vertical distances from the points to the line\nB. OLS minimises the sum of the squares of the vertical distances from the points to the line\nC. OLS minimises the sum of the horizontal distances from the points to the line\nD. OLS minimises the sum of the squares of the horizontal distances from the points to the line.\nAnswer:", " OLS minimises the sum of the squares of the vertical distances from the points to the line"], ["Question: Which of the following statements is TRUE concerning OLS estimation?\nChoices:\nA. OLS minimises the sum of the vertical distances from the points to the line\nB. OLS minimises the sum of the squares of the vertical distances from the points to the line\nC. OLS minimises the sum of the horizontal distances from the points to the line\nD. OLS minimises the sum of the squares of the horizontal distances from the points to the line.\nAnswer:", " OLS minimises the sum of the horizontal distances from the points to the line"], ["Question: Which of the following statements is TRUE concerning OLS estimation?\nChoices:\nA. OLS minimises the sum of the vertical distances from the points to the line\nB. OLS minimises the sum of the squares of the vertical distances from the points to the line\nC. OLS minimises the sum of the horizontal distances from the points to the line\nD. OLS minimises the sum of the squares of the horizontal distances from the points to the line.\nAnswer:", " OLS minimises the sum of the squares of the horizontal distances from the points to the line."]]