Which one of the following is the most appropriate definition of a 99% confidence interval?<n>A.99% of the time in repeated samples, the interval would contain the true value of the parameter<n>B.99% of the time in repeated samples, the interval would contain the estimated value of the parameter<n>C.99% of the time in repeated samples, the null hypothesis will be rejected<n>D.99% of the time in repeated samples, the null hypothesis will not be rejected when it was false[SEP]A
What is the main difference between the Dickey Fuller (DF) and Phillips-Perron (PP) approaches to unit root testing?<n>A.ADF is a single equation approach to unit root testing while PP is a systems approach<n>B.PP tests reverse the DF null and alternative hypotheses so that there is stationarity under the null hypothesis of the PP test<n>C.The PP test incorporates an automatic correction for autocorrelated residuals in the test regression<n>D.PP tests have good power in small samples whereas DF tests do not.[SEP]C
If there were a leverage effect in practice, what would be the shape of the news impact curve for as model that accounted for that leverage?<n>A.It would rise more quickly for negative disturbances than for positive ones of the same magnitude<n>B.It would be symmetrical about zero<n>C.It would rise less quickly for negative disturbances than for positive ones of the same magnitude<n>D.It would be zero for all positive disturbances[SEP]A
Which of the following statements is false concerning the linear probability model?<n>A.There is nothing in the model to ensure that the estimated probabilities lie between zero and one<n>B.Even if the probabilities are truncated at zero and one, there will probably be many observations for which the probability is either exactly zero or exactly one<n>C.The error terms will be heteroscedastic and not normally distributed<n>D.The model is much harder to estimate than a standard regression model with a continuous dependent variable[SEP]D
Which of the following statements concerning the regression population and sample is FALSE?<n>A.The population is the total collection of all items of interest<n>B.The population can be infinite<n>C.In theory, the sample could be larger than the population<n>D.A random sample is one where each individual item from the population is equally likely to be drawn.[SEP]C
Which of the following statements is INCORRECT concerning a comparison of the Box-Pierce Q and the Ljung-Box Q* statistics for linear dependence in time series?<n>A.Asymptotically, the values of the two test statistics will be equal<n>B.The Q test has better small-sample properties than the Q*<n>C.The Q test is sometimes over-sized for small samples<n>D.As the sample size tends towards infinity, both tests will show a tendency to always reject the null hypothesis of zero autocorrelation coefficients.[SEP]B
A parsimonious model is one that<n>A.Includes too many variables<n>B.Includes as few variables as possible to explain the data<n>C.Is a well-specified model<n>D.Is a mis-specified model[SEP]A
Which of the following is NOT a feature of continuously compounded returns (i.e. log-returns)?<n>A.They can be interpreted as continuously compounded changes in the prices<n>B.They can be added over time to give returns for longer time periods<n>C.They can be added across a portfolio of assets to give portfolio returns<n>D.They are usually fat-tailed[SEP]C
Which of the following features of financial asset return time-series could be captured using a standard GARCH(1,1) model?<n><n>i) Fat tails in the return distribution<n><n><n>ii) Leverage effects<n><n><n>iii) Volatility clustering<n><n><n>iv) Volatility affecting returns<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Consider the estimation of a GARCH-M model. If the data employed were a time-series of daily corporate bond percentage returns, which of the following would you expect the value of the GARCH-in-mean parameter estimate to be?<n>A.Less than -1<n>B.Between -1 and 0<n>C.Between 0 and 1<n>D.Bigger than 1[SEP]C
Under which of the following situations would bootstrapping be preferred to pure simulation?<n><n>i) If it is desired that the distributional properties of the data in the experiment<n><n>are the same as those of some actual data<n><n><n>ii) If it is desired that the distributional properties of the data in the experiment<n><n>are known exactly<n><n><n>iii) If the distributional properties of the actual data are unknown<n><n><n>iv) If the sample of actual data available is very small<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iv) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Which of the following may be consequences of one or more of the CLRM assumptions being violated?<n><n>i) The coefficient estimates are not optimal<n><n><n>ii) The standard error estimates are not optimal<n><n><n>iii) The distributions assumed for the test statistics are inappropriate<n><n><n>iv) Conclusions regarding the strength of relationships between the dependent<n><n>and independent variables may be invalid.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]D
Which of the following statements is true concerning forecasting in econometrics?<n>A.Forecasts can only be made for time-series data<n>B.Mis-specified models are certain to produce inaccurate forecasts<n>C.Structural forecasts are simpler to produce than those from time series models<n>D.In-sample forecasting ability is a poor test of model adequacy[SEP]D
The pacf is necessary for distinguishing between<n>A.An AR and an MA model<n>B.An AR and an ARMA model<n>C.An MA and an ARMA model<n>D.Different models from within the ARMA family[SEP]B
Negative residual autocorrelation is indicated by which one of the following?<n>A.A cyclical pattern in the residuals<n>B.An alternating pattern in the residuals<n>C.A complete randomness in the residuals<n>D.Residuals that are all close to zero[SEP]B
Which of the following statements are true concerning a comparison between ARCH(q) and GARCH(1,1) models?<n><n>i) The ARCH(q) model is likely to be the more parsimonious<n><n><n>ii) The ARCH(q) model is the more likely to violate non-negativity constraints<n><n><n>iii) The ARCH(q) model can allow for an infinite number of previous lags of squared<n><n>returns to affect the current conditional variance<n><n><n>iv) The GARCH(1,1) model will usually be sufficient to capture all of the dependence<n><n>in the conditional variance<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
Which of the following statements are true concerning a triangular or recursive system?<n><n>i) The parameters can be validly estimated using separate applications of OLS to<n><n>each equation<n><n><n>ii) The independent variables may be correlated with the error terms in other<n><n>equations<n><n><n>iii) An application of 2SLS would lead to unbiased but inefficient parameter estimates<n><n><n>iv) The independent variables may be correlated with the error terms in the equations<n><n>in which they appear as independent variables<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Which one of the following statements best describes the algebraic representation of the fitted regression line?<n>A.\hat{y}_t = \hat{\alpha} + \hat{\beta}x_t + \hat{u}_t<n>B.\hat{y}_t = \hat{\alpha} + \hat{\beta}x_t<n>C.\hat{y}_t = \hat{\alpha} + \hat{\beta}x_t + u_t<n>D.y_t = \hat{\alpha} + \hat{\beta}x_t + \hat{u}_t[SEP]B
What are the dimensions of $\hat{u}^t \hat{u}?<n>A.T x k<n>B.T x 1<n>C.k x 1<n>D.1 x 1[SEP]D
The characteristic roots of the MA process<n><n>$y_t = -3u_{t-1} + u_{t-2} + u_t$<n><n>are<n>A.1 and 2<n>B.1 and 0.5<n>C.2 and -0.5<n>D.1 and -3[SEP]B
Which of the following is an equivalent expression for saying that the explanatory variable is "non-stochastic"?<n>A.The explanatory variable is partly random<n>B.The explanatory variable is fixed in repeated samples<n>C.The explanatory variable is correlated with the errors<n>D.The explanatory variable always has a value of one[SEP]B
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?<n>A.Residuals appear to be positively autocorrelated<n>B.Residuals appear to be negatively autocorrelated<n>C.Residuals appear not to be autocorrelated<n>D.The test result is inconclusive[SEP]D
If OLS is used in the presence of autocorrelation, which of the following will be likely consequences?<n><n>i) Coefficient estimates may be misleading<n><n><n>ii) Hypothesis tests could reach the wrong conclusions<n><n><n>iii) Forecasts made from the model could be biased<n><n><n>iv) Standard errors may inappropriate<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
What will be the properties of the OLS estimator in the presence of multicollinearity?<n>A.It will be consistent, unbiased and efficient<n>B.It will be consistent and unbiased but not efficient<n>C.It will be consistent but not unbiased<n>D.It will not be consistent[SEP]A
Which one of the following would NOT be a consequence of using non-stationary data in levels form?<n>A.The regression $R^2$ may be spuriously high<n>B.Test statistics may not follow standard distributions<n>C.Statistical inferences may be invalid<n>D.Parameter estimates may be biased[SEP]D
If a series, y, follows a random walk, what is the optimal one-step ahead forecast of y?<n>A.The current value of y<n>B.Zero<n>C.One<n>D.The average value of y over the in-sample period[SEP]A
The order condition is<n>A.A necessary and sufficient condition for identification<n>B.A necessary but not sufficient condition for identification<n>C.A sufficient but not necessary condition for identification<n>D.A condition that is nether necessary nor sufficient for identification[SEP]B
If an estimator is said to have minimum variance, which of the following statements is NOT implied?<n>A.The probability that the estimate is a long way away from its true value is minimised<n>B.The estimator is efficient<n>C.Such an estimator would be termed "best"<n>D.Such an estimator will always be unbiased[SEP]D
Which of the following are disadvantages of the Dickey-Fuller / Engle-Granger approach to testing for cointegration and modelling cointegrating relationships?<n><n>i) Only one cointegrating relationship can be estimated<n><n>ii) Particularly for small samples. There is a high chance of the tests suggestingthat variables are not cointegrated when they are<n><n>iii) It is not possible to make inferences on the cointegrating regression<n><n>iv) The procedure forces the researcher to specify which is the dependent variable and which are the independent variables.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]D
Which of the following statements is true concerning the population regression function (PRF) and sample regression function (SRF)?<n>A.The PRF is the estimated model<n>B.The PRF is used to infer likely values of the SRF<n>C.Whether the model is good can be determined by comparing the SRF and the PRF<n>D.The PRF is a description of the process thought to be generating the data.[SEP]D
Which one of the following is a disadvantage of the general to specific or "LSE" ("Hendry") approach to building econometric models, relative to the specific to general approach?<n>A.Some variables may be excluded at the first stage leading to coefficient biases<n>B.The final model may lack theoretical interpretation<n>C.The final model may be statistically inadequate<n>D.If the initial model is mis-specified, all subsequent steps will be invalid.[SEP]B
Which of the following statements are true concerning maximum likelihood (ML) estimation in the context of GARCH models?<n><n>i) Maximum likelihood estimation selects the parameter values that maximise the<n><n>probability that we would have actually observed the values of the series y that we<n><n>actually did.<n><n><n>ii) GARCH models can only be estimated by ML and not by OLS<n><n><n>iii) For estimation of a standard linear model (with no GARCH), the OLS and ML<n><n>estimates for the slope and intercept parameters will be identical but the estimator<n><n>for the variance of the disturbances is slightly different<n><n><n>iv) Most computer packages use numerical procedures to estimate GARCH models<n><n>rather than a set of analytical formulae<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]D
Including relevant lagged values of the dependent variable on the right hand side of a regression equation could lead to which one of the following?<n>A.Biased but consistent coefficient estimates<n>B.Biased and inconsistent coefficient estimates<n>C.Unbiased but inconsistent coefficient estimates<n>D.Unbiased and consistent but inefficient coefficient estimates.[SEP]A
Which one of the following factors is likely to lead to a relatively high degree of out-of-sample forecast accuracy?<n>A.A model that is based on financial theory<n>B.A model that contains many variables<n>C.A model whose dependent variable has recently exhibited a structural change<n>D.A model that is entirely statistical in nature with no room for judgmental modification of forecasts[SEP]A
Which of the following are plausible approaches to dealing with residual autocorrelation?<n><n>i) Take logarithms of each of the variables<n><n>ii) Add lagged values of the variables to the regression equation<n><n>iii) Use dummy variables to remove outlying observations<n><n>iv) Try a model in first differenced form rather than in levels.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
For an autoregressive process to be considered stationary<n>A.The roots of the characteristic equation must all lie inside the unit circle<n>B.The roots of the characteristic equation must all lie on the unit circle<n>C.The roots of the characteristic equation must all lie outside the unit circle<n>D.The roots of the characteristic equation must all be less than one in absolute value[SEP]C
Which of the following statements are true concerning information criteria?<n><n>(i) Adjusted R-squared is an information criterion<n><n>(ii) If the residual sum of squares falls when an additional term is added, the value of the information criterion will fall<n><n>(iii) Akaike's information criterion always leads to model orders that are at least as large as those of Schwarz's information criterion<n><n>(iv) Akaike's information criterion is consistent<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
The "within transform" involves<n>A.Taking the average values of the variables<n>B.Subtracting the mean of each entity away from each observation on that entity<n>C.Estimating a panel data model using least squares dummy variables<n>D.Using both time dummies and cross-sectional dummies in a fixed effects panel model[SEP]B
The purpose of "augmenting" the Dickey-Fuller test regression is to<n>A.Ensure that there is no heteroscedasticity in the test regression residuals.<n>B.Ensure that the test regression residuals are normally distributed<n>C.Ensure that there is no autocorrelation in the test regression residuals<n>D.Ensure that all of the non-stationarity is taken into account.[SEP]C
If a series, y, follows a random walk with drift b, what is the optimal one-step ahead forecast of the change in y?<n>A.The current value of y<n>B.Zero<n>C.One<n>D.The average value of the change in y over the in-sample period[SEP]D
Which of the following are plausible approaches to dealing with a model that exhibits heteroscedasticity?<n><n>i) Take logarithms of each of the variables<n><n>ii) Use suitably modified standard errors<n><n>iii) Use a generalised least squares procedure<n><n>iv) Add lagged values of the variables to the regression equation.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
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><n>i) They are assumed to be normally distributed<n><n><n>ii) Their squares will be related to their lagged squared values if the GARCH model is<n><n>appropriate<n><n><n>iii) In practice, they are likely to have fat tails<n><n><n>iv) If the GARCH model is adequate, the standardised residuals and the raw residuals<n><n>will be identical<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Which one of the following statements is true concerning VARs?<n>A.The coefficient estimates have intuitive theoretical interpretations<n>B.The coefficient estimates usually have the same sign for all of the lags of a given variable in a given equation<n>C.VARs often produce better forecasts than simultaneous equation structural models<n>D.All of the components of a VAR must be stationary before it can be used for forecasting[SEP]C
Which of the following statements is INCORRECT concerning the classical hypothesis testing framework?<n>A.If the null hypothesis is rejected, the alternative is accepted<n>B.The null hypothesis is the statement being tested while the alternative encompasses the remaining outcomes of interest<n>C.The test of significance and confidence interval approaches will always give the same conclusions<n>D.Hypothesis tests are used to make inferences about the population parameters.[SEP]A
An ARMA(p,q) (p, q are integers bigger than zero) model will have<n>A.An acf and pacf that both decline geometrically<n>B.An acf that declines geometrically and a pacf that is zero after p lags<n>C.An acf that declines geometrically and a pacf that is zero after q lags<n>D.An acf that is zero after p lags and a pacf that is zero after q lags[SEP]A
Suppose that the following regression is estimated using 27 quarterly observations:<n><n>$y_t = \beta_1 + \beta_2 x_2 + \beta_3 x_{3t} + u_t$<n><n>What is the appropriate critical value for a 2-sided 5% size of test of $H_0: \beta_3 = 1$?<n>A.1.64<n>B.1.71<n>C.2.06<n>D.1.96[SEP]C
Suppose that two researchers, using the same 3 variables and the same 250 observations on each variable, estimate a VAR. One estimates a VAR(6), while the other estimates a VAR(4). The determinants of the variance-covariance matrices of the residuals for each VAR are 0.0036 and 0.0049 respectively. What is the values of the test statistic for performing a test of whether the VAR(6) can be restricted to a VAR(4)?<n>A.77.07<n>B.0.31<n>C.0.33<n>D.4.87[SEP]A
Which of the following is a DISADVANTAGE of using pure time-series models (relative to structural models)?<n>A.They are not theoretically motivated<n>B.They cannot produce forecasts easily<n>C.They cannot be used for very high frequency data<n>D.It is difficult to determine the appropriate explanatory variables for use in pure time-series models[SEP]A
Which of the following are alternative names for the dependent variable (usually denoted by y) in linear regression analysis?<n><n>(i) The regressand<n><n>(ii) The regressor<n><n>(iii) The explained variable<n><n>(iv) The explanatory variable<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Which of the following are advantages of the VAR approach to modelling the relationship between variables relative to the estimation of full structural models?<n><n>i) VARs receive strong motivation from financial and economic theory<n><n><n>ii) VARs in their reduced forms can be used easily to produce time-series forecasts<n><n><n>iii) VAR models are typically highly parsimonious<n><n><n>iv) OLS can be applied separately to each equation in a reduced form VAR<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
Which of the following statements is TRUE concerning the standard regression model?<n>A.y has a probability distribution<n>B.x has a probability distribution<n>C.The disturbance term is assumed to be correlated with x<n>D.For an adequate model, the residual (u-hat) will be zero for all sample data points[SEP]A
Consider the following model for $y_t$:<n><n>$y_t = \mu + \lambda t + u_t$<n><n>Which one of the following most accurately describes the process for $y_t$?<n>A.A unit root process<n>B.A stationary process<n>C.A deterministic trend process<n>D.A random walk with drift[SEP]C
Which of the following is correct concerning logit and probit models?<n>A.They use a different method of transforming the model so that the probabilities lie between zero and one<n>B.The logit model can result in too many observations falling at exactly zero or exactly one<n>C.For the logit model, the marginal effect of a change in one of the explanatory variables is simply the estimate of the parameter attached to that variable, whereas this is not the case for the probit model<n>D.The probit model is based on a cumulative logistic function[SEP]A
What is the most important disadvantage of the diagonal VECH approach to building multivariate GARCH models that is overcome by the BEKK formulation?<n>A.The diagonal VECH model is hard to interpret intuitively<n>B.The diagonal VECH model contains too many parameters<n>C.The diagonal VECH model does not ensure a positive-definite variance-covariance matrix<n>D.The BEKK model reduces the dimensionality problem that arises when a number of series are modelled together.[SEP]C
If a relevant variable is omitted from a regression equation, the consequences would be that:<n><n>i) The standard errors would be biased<n><n><n>ii) If the excluded variable is uncorrelated with all of the included variables, all of<n><n>the slope coefficients will be inconsistent.<n><n><n>iii) If the excluded variable is uncorrelated with all of the included variables, the<n><n>intercept coefficient will be inconsistent.<n><n><n>iv) If the excluded variable is uncorrelated with all of the included variables, all of<n><n>the slope and intercept coefficients will be consistent and unbiased but inefficient.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Which of the following are alternative names for the independent variable (usually denoted by x) in linear regression analysis?<n><n>(i) The regressor<n><n>(ii) The regressand<n><n>(iii) The causal variable<n><n>(iv) The effect variable<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Consider the OLS estimator for the standard error of the slope coefficient. Which of the following statement(s) is (are) true?<n><n>(i) The standard error will be positively related to the residual variance<n><n>(ii) The standard error will be negatively related to the dispersion of the observations on the explanatory variable about their mean value<n><n>(iii) The standard error will be negatively related to the sample size<n><n>(iv) The standard error gives a measure of the precision of the coefficient estimate.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]D
What is the meaning of the term "heteroscedasticity"?<n>A.The variance of the errors is not constant<n>B.The variance of the dependent variable is not constant<n>C.The errors are not linearly independent of one another<n>D.The errors have non-zero mean[SEP]A
If a Durbin Watson statistic takes a value close to zero, what will be the value of the first order autocorrelation coefficient?<n>A.Close to zero<n>B.Close to plus one<n>C.Close to minus one<n>D.Close to either minus one or plus one[SEP]C
Under the null hypothesis of a Bera-Jarque test, the distribution has<n>A.Zero skewness and zero kurtosis<n>B.Zero skewness and a kurtosis of three<n>C.Skewness of one and zero kurtosis<n>D.Skewness of one and kurtosis of three.[SEP]B
If an estimator is said to be consistent, it is implied that<n>A.On average, the estimated coefficient values will equal the true values<n>B.The OLS estimator is unbiased and no other unbiased estimator has a smaller variance<n>C.The estimates will converge upon the true values as the sample size increases<n>D.The coefficient estimates will be as close to their true values as possible for small and large samples.[SEP]C
Which of the following is a typical characteristic of financial asset return time-series?<n>A.Their distributions are thin-tailed<n>B.They are not weakly stationary<n>C.They are highly autocorrelated<n>D.They have no trend[SEP]D
Which of the following assumptions are required to show the consistency, unbiasedness and efficiency of the OLS estimator?<n><n>i) $E(u_t) = 0$<n><n><n>ii) $\text{Var}(u_t) = \sigma^2$<n><n><n>iii) $\text{Cov}(u_t, u_{t-j}) = 0 \forall j$<n><n><n>iv) $u_t \sim N(0, \sigma^2)$<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?<n>A.The model is likely to be technical to estimate<n>B.The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables<n>C.The number of parameters to estimate may be large, resulting in a loss of degrees of freedom<n>D.The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable.[SEP]C
Consider an identical situation to that of question 21, except that now a 2-sided alternative is used. What would now be the appropriate conclusion?<n>A.H0 is rejected<n>B.H0 is not rejected<n>C.H1 is rejected<n>D.There is insufficient information given in the question to reach a conclusion[SEP]A
The price of a house is best described as what type of number?<n>A.Discrete<n>B.Cardinal<n>C.Ordinal<n>D.Nominal[SEP]B
If a Johansen "trace" test for a null hypothesis of 2 cointegrating vectors is applied to a system containing 4 variables is conducted, which eigenvalues would be used in the test?<n>A.All of them<n>B.The largest 2<n>C.The smallest 2<n>D.The second largest[SEP]C
Which of the following statements is true concerning variance decomposition analysis of VARs?<n><n>i) Variance decompositions measure the impact of a unit shock to each of the variables on the VAR<n><n>ii) Variance decompositions can be thought of as measuring the proportion of the forecast error variance that is attributable to each variable<n><n>iii) The ordering of the variables is important for calculating impulse responses but not variance decompositions<n><n>iv) It is usual that most of the forecast error variance for a given variable is attributable to shocks to that variable<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
Suppose that we have estimated a GARCH model for daily equity returns, and we are interested in producing a 10-day forecast of the volatility (measured by the standard deviation of returns) for use in a value at risk model. How could such a forecast most validly be calculated?<n>A.Produce 1, 2, 3, ..., 10 step ahead conditional variance forecasts and add them up<n>B.Produce 1, 2, 3, ..., 10 step ahead conditional variance forecasts and add them up and take the square root<n>C.Produce 1, 2, 3, ..., 10 step ahead conditional variance forecasts, take the square roots of each one and add them up<n>D.Produce a 1-step ahead conditional variance forecast, take its square root and multiply it by the square root of 10[SEP]B
Suppose that the value of $R^2$ for an estimated regression model is exactly zero. Which of the following are true?<n><n>i) All coefficient estimates on the slopes will be zero<n><n>ii) The fitted line will be horizontal with respect to all of the explanatory variables<n><n>iii) The regression line has not explained any of the variability of y about its mean value<n><n>iv) The intercept coefficient estimate must be zero.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
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<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
Which of the following statements are true concerning the Box-Jenkins approach to diagnostic testing for ARMA models?<n><n>(i) The tests will show whether the identified model is either too large or too small<n><n>(ii) The tests involve checking the model residuals for autocorrelation, heteroscedasticity, and non-normality<n><n>(iii) If the model suggested at the identification stage is appropriate, the acf and pacf for the residuals should show no additional structure<n><n>(iv) If the model suggested at the identification stage is appropriate, the coefficients on the additional variables under the overfitting approach will be statistically insignificant<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
Which one of the following would be a plausible response to a finding of residual non-normality?<n>A.Use a logarithmic functional form instead of a linear one<n>B.Add lags of the variables on the right hand side of the regression model<n>C.Estimate the model in first differenced form<n>D.Remove any large outliers from the data.[SEP]D
The fixed effects panel model is also sometimes known as<n>A.A seemingly unrelated regression model<n>B.The least squares dummy variables approach<n>C.The random effects model<n>D.Heteroscedasticity and autocorrelation consistent[SEP]B
Which of the following statements is TRUE concerning OLS estimation?<n>A.OLS minimises the sum of the vertical distances from the points to the line<n>B.OLS minimises the sum of the squares of the vertical distances from the points to the line<n>C.OLS minimises the sum of the horizontal distances from the points to the line<n>D.OLS minimises the sum of the squares of the horizontal distances from the points to the line.[SEP]B
If the standard tools for time-series analysis, such as estimation of the acf, pacf and spectral analysis, find no evidence of structure in the data, this implies that the data are which of the following?<n>A.Normally distributed<n>B.Uncorrelated<n>C.Independent<n>D.Fat-tailed[SEP]B
If two variables, $x_t$ and $y_t$ are said to be cointegrated, which of the following statements are true?<n><n>i) $x_t$ and $y_t$ must both be stationary<n><n><n>ii) Only one linear combination of $x_t$ and $y_t$ will be stationary<n><n><n>iii) The cointegrating equation for $x_t$ and $y_t$ describes the short-run relationship<n><n>between the two series<n><n><n>iv) The residuals of a regression of $y_t$ on $x_t$ must be stationary<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]A
A dependent variable whose values are not observable outside a certain range but where the corresponding values of the independent variables are still available would be most accurately described as what kind of variable?<n>A.Censored<n>B.Truncated<n>C.Multinomial variable<n>D.Discrete choice[SEP]A
A Hausman test would be used for<n>A.Determining whether an equation that is part of a simultaneous system is identified<n>B.Determining whether a simultaneous framework is needed for a particular variable<n>C.Determining whether 2SLS or ILS is optimal<n>D.Determining whether the structural form equations can be obtained via substitution from the reduced forms[SEP]B
Under the matrix notation for the classical linear regression model, $y = X \beta + u$, what are the dimensions of $u$?<n>A.T x k<n>B.T x 1<n>C.k x 1<n>D.1 x 1[SEP]B
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?<n>A.12<n>B.4<n>C.3<n>D.36[SEP]D
A researcher tests for structural stability in the following regression model:<n><n>$y_t = \beta_1 + \beta_2 x_{2t} + \beta_3 x_{3t} + u_t$<n><n>The total sample of 200 observations is split exactly in half for the sub-sample regressions. Which would be the unrestricted residual sum of squares?<n>A.The RSS for the whole sample<n>B.The RSS for the first sub-sample<n>C.The RSS for the second sub-sample<n>D.The sum of the RSS for the first and second sub-samples[SEP]D
Suppose that we are interested in testing the null hypothesis that a GARCH(2,2) model can be restricted to a process with a constant conditional variance using the likelihood ratio test approach. Which of the following statements are true?<n>A.The test statistic will follow a chi-squared distribution with 2 degrees of freedom under the null hypothesis<n>B.The value of the log-likelihood function will almost always be bigger for the restricted model than for the unrestricted model<n>C.If the relevant values of the log-likelihood functions are -112.3 and -118.4, the value of the test statistic is 12.2<n>D.The likelihood ratio test compares the slopes of the log-likelihood function at the maximum and at the restricted parameter value.[SEP]C
Which one of the following is NOT a plausible remedy for near multicollinearity?<n>A.Use principal components analysis<n>B.Drop one of the collinear variables<n>C.Use a longer run of data<n>D.Take logarithms of each of the variables[SEP]D
Consider the following AR(2) process:<n><n>yt = 1.5 yt-1 - 0.5 yt-2 + ut<n><n>This is a<n>A.Stationary process<n>B.Unit root process<n>C.Explosive process<n>D.Stationary and unit root process[SEP]B
Which of the following could be used as a test for autocorrelation up to third order?<n>A.The Durbin Watson test<n>B.White's test<n>C.The RESET test<n>D.The Breusch-Godfrey test[SEP]D
The residual from a standard regression model is defined as<n>A.The difference between the actual value, y, and the mean, y-bar<n>B.The difference between the fitted value, y-hat, and the mean, y-bar<n>C.The difference between the actual value, y, and the fitted value, y-hat<n>D.The square of the difference between the fitted value, y-hat, and the mean, y-bar[SEP]C
If OLS is applied separately to each equation that is part of a simultaneous system, the resulting estimates will be<n>A.Unbiased and consistent<n>B.Biased but consistent<n>C.Biased and inconsistent<n>D.It is impossible to apply OLS to equations that are part of a simultaneous system[SEP]C
Which one of the following is NOT an example of mis-specification of functional form?<n>A.Using a linear specification when y scales as a function of the squares of x<n>B.Using a linear specification when a double-logarithmic model would be more appropriate<n>C.Modelling y as a function of x when in fact it scales as a function of 1/x<n>D.Excluding a relevant variable from a linear regression model[SEP]D
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?<n>A.A logit model<n>B.A multinomial logit<n>C.A tobit model<n>D.An ordered logit model[SEP]B
Which of the following statements will be true if the number of replications used in a Monte Carlo study is small?<n><n>i) The statistic of interest may be estimated imprecisely<n><n><n>ii) The results may be affected by unrepresentative combinations of random draws<n><n><n>iii) The standard errors on the estimated quantities may be unacceptably large<n><n><n>iv) Variance reduction techniques can be used to reduce the standard errors<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iv) only<n>D.(i), (ii), (iii), and (iv)[SEP]D
Which of the following is a disadvantage of the random effects approach to estimating a panel model?<n>A.The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables<n>B.The number of parameters to estimate may be large, resulting in a loss of degrees of freedom<n>C.The random effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable.<n>D.All of (a) to (c) are potential disadvantages of the random effects approach.[SEP]A
Which of the following could result in autocorrelated residuals?<n><n>i) Slowness of response of the dependent variable to changes in the values of the independent variables<n><n>ii) Over-reactions of the dependent variable to changes in the independent variables<n><n>iii) Omission of relevant explanatory variables that are autocorrelated<n><n>iv) Outliers in the data<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Which of the following statements are true concerning the acf and pacf?<n><n>(i) The acf and pacf are often hard to interpret in practice<n><n>(ii) The acf and pacf can be difficult to calculate for some data sets<n><n>(iii) Information criteria represent an alternative approach to model order determination<n><n>(iv) If applied correctly, the acf and pacf will always deliver unique model selections<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Which of the following conditions are necessary for a series to be classifiable as a weakly stationary process?<n><n>(i) It must have a constant mean<n><n>(ii) It must have a constant variance<n><n>(iii) It must have constant autocovariances for given lags<n><n>(iv) It must have a constant probability distribution<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Consider the following equation and determine the class of model that it best represents<n><n>$y_{it} = \alpha + \beta_{it} + \mu_i + \nu_{it}$<n>A.An entity fixed effects model<n>B.A time fixed effects model<n>C.A random effects model<n>D.A pure time series model[SEP]A
Note that statistical tables are not necessary to answer this question. For a sample of 1000 observations, the Dickey-Fuller test statistic values are<n>A.More negative than (i.e. bigger in absolute value than) those in the left hand tail of a normal distribution<n>B.Less negative than (i.e. smaller in absolute value than) those in the left hand tail of a normal distribution<n>C.Obtained from an analytical formula for the density of the Dickey-Fuller distribution<n>D.More negative (i.e. bigger in absolute value) for a 10% size of test than a 5% test.[SEP]A
Suppose that a hypothesis test is conducted using a 5% significance level. Which of the following statements are correct?<n><n>(i) The significance level is equal to the size of the test<n><n>(ii) The significance level is equal to the power of the test<n><n>(iii) 2.5% of the total distribution will be in each tail rejection region for a 2-sided test<n><n>(iv) 5% of the total distribution will be in each tail rejection region for a 2-sided test.<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Which one of the following criticisms of the Dickey-Fuller/Engle-Granger approach to dealing with cointegrated variables is overcome by the Engle-Yoo (EY) procedure?<n>A.In the context of small samples, Dickey Fuller tests are prone to conclude that there is a unit root in a series when there is not<n>B.The Engle-Granger (EG) approach can only detect up to one cointegrating relationship even though there could be more than one<n>C.The variables are treated asymmetrically in the cointegrating tests<n>D.It is not possible to perform tests about the cointegrating relationship[SEP]D
Consider a series that follows an MA(1) with zero mean and a moving average coefficient of 0.4. What is the value of the autocovariance at lag 1?<n>A.0.4<n>B.1<n>C.0.34<n>D.It is not possible to determine the value of the autocovariances without knowing the disturbance variance.[SEP]D
Which of the following estimation techniques are available for the estimation of over-identified systems of simultaneous equations?<n><n>i) OLS<n><n>ii) ILS<n><n>iii) 2SLS<n><n>iv) IV<n>A.(iii) only<n>B.(iii) and (iv) only<n>C.(ii), (iii), and (iv) only<n>D.(i), (ii), (iii) and (iv)[SEP]B
Which one of the following statements best describes a Type II error?<n>A.It is the probability of incorrectly rejecting the null hypothesis<n>B.It is equivalent to the power of the test<n>C.It is equivalent to the size of the test<n>D.It is the probability of failing to reject a null hypothesis that was wrong[SEP]D
Which one of the following would be the most appropriate as a 95% (two-sided) confidence interval for the intercept term of the model given in question 21?<n>A.(-4.79,2.19)<n>B.(-4.16,4.16)<n>C.(-1.98,1.98)<n>D.(-5.46,2.86)[SEP]D
Which of the following are characteristics of a stationary process?<n><n>i) It crosses its mean value frequently<n><n><n>ii) It has constant mean and variance<n><n><n>iii) It contains no trend component<n><n><n>iv) It will be stationary in first difference form<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]D
Consider again the VAR model of equation 16. Which of the following conditions must hold for it to be said that there is bi-directional feedback?<n>A.The b and d coefficients significant and the a and c coefficients insignificant<n>B.The a and c coefficients significant and the b and d coefficients insignificant<n>C.The a and c coefficients significant<n>D.The b and d coefficients significant[SEP]D
Consider the following sample autocorrelation estimates obtained using 250 data points:<n><n>1) Lag 1 2 3<n><n>2) Coefficient 0.2 -0.15 -0.1<n><n>3) Assuming that the coefficients are approximately normally distributed, which of the coefficients are statistically significant at the 5% level?<n>A.1 only<n>B.1 and 2 only<n>C.1, 2 and 3 only<n>D.It is not possible to determine the statistical significance since no standard errors have been given[SEP]B
Which one of the following is examined by looking at a goodness of fit statistic?<n>A.How well the population regression function fits the data<n>B.How well the sample regression function fits the population regression function<n>C.How well the sample regression function fits the data<n>D.How well the population regression function fits the sample regression function.[SEP]C
Which of the following statements are correct concerning the use of antithetic variates as part of a Monte Carlo experiment?<n><n>i) Antithetic variates work by reducing the number of replications required to cover the whole probability space<n><n>ii) Antithetic variates involve employing a similar variable to that used in the simulation, but whose properties are known analytically<n><n>iii) Antithetic variates involve using the negative of each of the random draws and repeating the experiment using those values as the draws<n><n>iv) Antithetic variates involve taking one over each of the random draws and repeating the experiment using those values as the draws<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iv) only<n>D.(i), (ii), (iii), and (iv)[SEP]B
Which one of the following statements is true concerning alternative forecast accuracy measures?<n>A.Mean squared error is usually highly correlated with trading rule profitability<n>B.Mean absolute error provides a quadratic loss function<n>C.Mean absolute percentage error is a useful measure for evaluating asset return forecasts<n>D.Mean squared error penalises large forecast errors disproportionately more than small forecast errors[SEP]D
Which of the following criticisms of standard ("plain vanilla") GARCH models can be overcome by EGARCH models?<n><n>i) Estimated coefficient values from GARCH models may be negative<n><n>ii) GARCH models cannot account for leverage effects<n><n>iii) The responsiveness of future volatility to positive and negative shocks is symmetric under a GARCH formulation<n><n>iv) GARCH models cannot allow for a feedback from the volatility to the returns<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C
Suppose that 100 separate firms were tested to determine how many of them "beat the market" using a Jensen-type regression, and it is found that 3 fund managers significantly do so. Does this suggest prima facie evidence for stock market inefficiency?<n>A.Yes<n>B.No<n>C.In order to answer this question, you would need to test every fund manager trading in that market<n>D.There is insufficient information given in the question to draw a conclusion about market efficiency.[SEP]B
Which of the following are advantages of the use of panel data over pure cross-sectional or pure time-series modelling?<n><n>(i) The use of panel data can increase the number of degrees of freedom and therefore the power of tests<n><n>(ii) The use of panel data allows the average value of the dependent variable to vary either cross-sectionally or over time or both<n><n>(iii) The use of panel data enables the researcher allows the estimated relationship between the independent and dependent variables to vary either cross-sectionally or over time or both<n>A.(i) only<n>B.(i) and (ii) only<n>C.(ii) only<n>D.(i), (ii), and (iii)[SEP]B
If the Engle-Granger test is applied to the residuals of a potentially cointegrating regression, what would be the interpretation of the null hypothesis?<n>A.The variables are cointegrated<n>B.The variables are not cointegrated<n>C.Both variables are stationary<n>D.Both variables are non-stationary[SEP]B
Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?<n><n>i) The acf and pacf will always be identical at lag one whatever the model<n><n>ii) The pacf for an MA(q) model will in general be non-zero beyond lag q<n><n>iii) The pacf for an AR(p) model will be zero beyond lag p<n><n>iv) The acf and pacf will be the same at lag two for an MA(1) model<n>A.(ii) and (iv) only<n>B.(i) and (iii) only<n>C.(i), (ii), and (iii) only<n>D.(i), (ii), (iii), and (iv)[SEP]C