[ { "input": { "text": "Consider the following time series model applied to daily data:\n\n\n\nwhere rt are the returns, and D1, D2, D3 and D4 are dummy variables. D1 = 1 on Monday and zero otherwise; D2 = 1 on Tuesday and zero otherwise, ..., D4 = 1 on Thursday and zero otherwise. What is the interpretation of the parameter estimate for the intercept?" }, "references": [ { "output": { "text": "It is the average return on Friday" }, "tags": [ "correct" ] }, { "output": { "text": "It is the average return on Monday" }, "tags": [] }, { "output": { "text": "It is the Friday deviation from the mean return for the week" }, "tags": [] }, { "output": { "text": "It is the Monday deviation from the mean return for the week." }, "tags": [] } ], "split": "valid", "id": "id13" }, { "input": { "text": "The purpose of \"augmenting\" the Dickey-Fuller test regression is to" }, "references": [ { "output": { "text": "Ensure that there is no heteroscedasticity in the test regression residuals." }, "tags": [] }, { "output": { "text": "Ensure that the test regression residuals are normally distributed" }, "tags": [] }, { "output": { "text": "Ensure that there is no autocorrelation in the test regression residuals" }, "tags": [ "correct" ] }, { "output": { "text": "Ensure that all of the non-stationarity is taken into account." }, "tags": [] } ], "split": "test", "id": "id55" }, { "input": { "text": "Which of the following statements are true concerning maximum likelihood (ML) estimation in the context of GARCH models?\n\ni) Maximum likelihood estimation selects the parameter values that maximise the\n\nprobability that we would have actually observed the values of the series y that we\n\nactually did.\n\n\nii) GARCH models can only be estimated by ML and not by OLS\n\n\niii) For estimation of a standard linear model (with no GARCH), the OLS and ML\n\nestimates for the slope and intercept parameters will be identical but the estimator\n\nfor the variance of the disturbances is slightly different\n\n\niv) Most computer packages use numerical procedures to estimate GARCH models\n\nrather than a set of analytical formulae" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [ "correct" ] } ], "split": "test", "id": "id48" }, { "input": { "text": "Which of the following statements is true concerning forecasting in econometrics?" }, "references": [ { "output": { "text": "Forecasts can only be made for time-series data" }, "tags": [] }, { "output": { "text": "Mis-specified models are certain to produce inaccurate forecasts" }, "tags": [] }, { "output": { "text": "Structural forecasts are simpler to produce than those from time series models" }, "tags": [] }, { "output": { "text": "In-sample forecasting ability is a poor test of model adequacy" }, "tags": [ "correct" ] } ], "split": "test", "id": "id29" }, { "input": { "text": "Consider the following model for $y_t$:\n\n$y_t = \\mu + \\lambda t + u_t$\n\nWhich one of the following most accurately describes the process for $y_t$?" }, "references": [ { "output": { "text": "A unit root process" }, "tags": [] }, { "output": { "text": "A stationary process" }, "tags": [] }, { "output": { "text": "A deterministic trend process" }, "tags": [ "correct" ] }, { "output": { "text": "A random walk with drift" }, "tags": [] } ], "split": "test", "id": "id68" }, { "input": { "text": "If OLS is applied separately to each equation that is part of a simultaneous system, the resulting estimates will be" }, "references": [ { "output": { "text": "Unbiased and consistent" }, "tags": [] }, { "output": { "text": "Biased but consistent" }, "tags": [] }, { "output": { "text": "Biased and inconsistent" }, "tags": [ "correct" ] }, { "output": { "text": "It is impossible to apply OLS to equations that are part of a simultaneous system" }, "tags": [] } ], "split": "test", "id": "id104" }, { "input": { "text": "Which one of the following statements is true concerning VARs?" }, "references": [ { "output": { "text": "The coefficient estimates have intuitive theoretical interpretations" }, "tags": [] }, { "output": { "text": "The coefficient estimates usually have the same sign for all of the lags of a given variable in a given equation" }, "tags": [] }, { "output": { "text": "VARs often produce better forecasts than simultaneous equation structural models" }, "tags": [ "correct" ] }, { "output": { "text": "All of the components of a VAR must be stationary before it can be used for forecasting" }, "tags": [] } ], "split": "test", "id": "id59" }, { "input": { "text": "Under the matrix notation for the classical linear regression model, $y = X \\beta + u$, what are the dimensions of $u$?" }, "references": [ { "output": { "text": "T x k" }, "tags": [] }, { "output": { "text": "T x 1" }, "tags": [ "correct" ] }, { "output": { "text": "k x 1" }, "tags": [] }, { "output": { "text": "1 x 1" }, "tags": [] } ], "split": "test", "id": "id96" }, { "input": { "text": "Near multicollinearity occurs when" }, "references": [ { "output": { "text": "Two or more explanatory variables are perfectly correlated with one another" }, "tags": [] }, { "output": { "text": "The explanatory variables are highly correlated with the error term" }, "tags": [] }, { "output": { "text": "The explanatory variables are highly correlated with the dependent variable" }, "tags": [] }, { "output": { "text": "Two or more explanatory variables are highly correlated with one another" }, "tags": [ "correct" ] } ], "split": "valid", "id": "id12" }, { "input": { "text": "Which of the following estimation techniques are available for the estimation of over-identified systems of simultaneous equations?\n\ni) OLS\n\nii) ILS\n\niii) 2SLS\n\niv) IV" }, "references": [ { "output": { "text": "(iii) only" }, "tags": [] }, { "output": { "text": "(iii) and (iv) only" }, "tags": [ "correct" ] }, { "output": { "text": "(ii), (iii), and (iv) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii) and (iv)" }, "tags": [] } ], "split": "test", "id": "id117" }, { "input": { "text": "If the residuals from a regression estimated using a small sample of data are not normally distributed, which one of the following consequences may arise?" }, "references": [ { "output": { "text": "The coefficient estimates will be unbiased but inconsistent" }, "tags": [] }, { "output": { "text": "The coefficient estimates will be biased but consistent" }, "tags": [] }, { "output": { "text": "The coefficient estimates will be biased and inconsistent" }, "tags": [] }, { "output": { "text": "Test statistics concerning the parameters will not follow their assumed distributions." }, "tags": [ "correct" ] } ], "split": "valid", "id": "id15" }, { "input": { "text": "Under the null hypothesis of a Bera-Jarque test, the distribution has" }, "references": [ { "output": { "text": "Zero skewness and zero kurtosis" }, "tags": [] }, { "output": { "text": "Zero skewness and a kurtosis of three" }, "tags": [ "correct" ] }, { "output": { "text": "Skewness of one and zero kurtosis" }, "tags": [] }, { "output": { "text": "Skewness of one and kurtosis of three." }, "tags": [] } ], "split": "test", "id": "id76" }, { "input": { "text": "The residual from a standard regression model is defined as" }, "references": [ { "output": { "text": "The difference between the actual value, y, and the mean, y-bar" }, "tags": [] }, { "output": { "text": "The difference between the fitted value, y-hat, and the mean, y-bar" }, "tags": [] }, { "output": { "text": "The difference between the actual value, y, and the fitted value, y-hat" }, "tags": [ "correct" ] }, { "output": { "text": "The square of the difference between the fitted value, y-hat, and the mean, y-bar" }, "tags": [] } ], "split": "test", "id": "id103" }, { "input": { "text": "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" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id53" }, { "input": { "text": "Which of the following statements is TRUE concerning OLS estimation?" }, "references": [ { "output": { "text": "OLS minimises the sum of the vertical distances from the points to the line" }, "tags": [] }, { "output": { "text": "OLS minimises the sum of the squares of the vertical distances from the points to the line" }, "tags": [ "correct" ] }, { "output": { "text": "OLS minimises the sum of the horizontal distances from the points to the line" }, "tags": [] }, { "output": { "text": "OLS minimises the sum of the squares of the horizontal distances from the points to the line." }, "tags": [] } ], "split": "test", "id": "id91" }, { "input": { "text": "Suppose that observations are available on the monthly bond prices of 100 companies for 5 years. What type of data are these?" }, "references": [ { "output": { "text": "Cross-sectional" }, "tags": [] }, { "output": { "text": "Time-series" }, "tags": [] }, { "output": { "text": "Panel" }, "tags": [ "correct" ] }, { "output": { "text": "Qualitative" }, "tags": [] } ], "split": "valid", "id": "id7" }, { "input": { "text": "Which one of the following is NOT an example of mis-specification of functional form?" }, "references": [ { "output": { "text": "Using a linear specification when y scales as a function of the squares of x" }, "tags": [] }, { "output": { "text": "Using a linear specification when a double-logarithmic model would be more appropriate" }, "tags": [] }, { "output": { "text": "Modelling y as a function of x when in fact it scales as a function of 1/x" }, "tags": [] }, { "output": { "text": "Excluding a relevant variable from a linear regression model" }, "tags": [ "correct" ] } ], "split": "test", "id": "id105" }, { "input": { "text": "Which one of the following would be a plausible response to a finding of residual non-normality?" }, "references": [ { "output": { "text": "Use a logarithmic functional form instead of a linear one" }, "tags": [] }, { "output": { "text": "Add lags of the variables on the right hand side of the regression model" }, "tags": [] }, { "output": { "text": "Estimate the model in first differenced form" }, "tags": [] }, { "output": { "text": "Remove any large outliers from the data." }, "tags": [ "correct" ] } ], "split": "test", "id": "id89" }, { "input": { "text": "Under which of the following situations would bootstrapping be preferred to pure simulation?\n\ni) If it is desired that the distributional properties of the data in the experiment\n\nare the same as those of some actual data\n\n\nii) If it is desired that the distributional properties of the data in the experiment\n\nare known exactly\n\n\niii) If the distributional properties of the actual data are unknown\n\n\niv) If the sample of actual data available is very small" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), and (iv) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id27" }, { "input": { "text": "Which one of the following factors is likely to lead to a relatively high degree of out-of-sample forecast accuracy?" }, "references": [ { "output": { "text": "A model that is based on financial theory" }, "tags": [ "correct" ] }, { "output": { "text": "A model that contains many variables" }, "tags": [] }, { "output": { "text": "A model whose dependent variable has recently exhibited a structural change" }, "tags": [] }, { "output": { "text": "A model that is entirely statistical in nature with no room for judgmental modification of forecasts" }, "tags": [] } ], "split": "test", "id": "id50" }, { "input": { "text": "An ARMA(p,q) (p, q are integers bigger than zero) model will have" }, "references": [ { "output": { "text": "An acf and pacf that both decline geometrically" }, "tags": [ "correct" ] }, { "output": { "text": "An acf that declines geometrically and a pacf that is zero after p lags" }, "tags": [] }, { "output": { "text": "An acf that declines geometrically and a pacf that is zero after q lags" }, "tags": [] }, { "output": { "text": "An acf that is zero after p lags and a pacf that is zero after q lags" }, "tags": [] } ], "split": "test", "id": "id61" }, { "input": { "text": "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?" }, "references": [ { "output": { "text": "12" }, "tags": [] }, { "output": { "text": "4" }, "tags": [] }, { "output": { "text": "3" }, "tags": [] }, { "output": { "text": "36" }, "tags": [ "correct" ] } ], "split": "test", "id": "id97" }, { "input": { "text": "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?" }, "references": [ { "output": { "text": "The test statistic will follow a chi-squared distribution with 2 degrees of freedom under the null hypothesis" }, "tags": [] }, { "output": { "text": "The value of the log-likelihood function will almost always be bigger for the restricted model than for the unrestricted model" }, "tags": [] }, { "output": { "text": "If the relevant values of the log-likelihood functions are -112.3 and -118.4, the value of the test statistic is 12.2" }, "tags": [ "correct" ] }, { "output": { "text": "The likelihood ratio test compares the slopes of the log-likelihood function at the maximum and at the restricted parameter value." }, "tags": [] } ], "split": "test", "id": "id99" }, { "input": { "text": "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\ni) VARs receive strong motivation from financial and economic theory\n\n\nii) VARs in their reduced forms can be used easily to produce time-series forecasts\n\n\niii) VAR models are typically highly parsimonious\n\n\niv) OLS can be applied separately to each equation in a reduced form VAR" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id66" }, { "input": { "text": "Which of the following statements concerning the regression population and sample is FALSE?" }, "references": [ { "output": { "text": "The population is the total collection of all items of interest" }, "tags": [] }, { "output": { "text": "The population can be infinite" }, "tags": [] }, { "output": { "text": "In theory, the sample could be larger than the population" }, "tags": [ "correct" ] }, { "output": { "text": "A random sample is one where each individual item from the population is equally likely to be drawn." }, "tags": [] } ], "split": "test", "id": "id21" }, { "input": { "text": "What are the dimensions of $\\hat{u}^t \\hat{u}?" }, "references": [ { "output": { "text": "T x k" }, "tags": [] }, { "output": { "text": "T x 1" }, "tags": [] }, { "output": { "text": "k x 1" }, "tags": [] }, { "output": { "text": "1 x 1" }, "tags": [ "correct" ] } ], "split": "test", "id": "id35" }, { "input": { "text": "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?" }, "references": [ { "output": { "text": "The b and d coefficients significant and the a and c coefficients insignificant" }, "tags": [] }, { "output": { "text": "The a and c coefficients significant and the b and d coefficients insignificant" }, "tags": [] }, { "output": { "text": "The a and c coefficients significant" }, "tags": [] }, { "output": { "text": "The b and d coefficients significant" }, "tags": [ "correct" ] } ], "split": "test", "id": "id121" }, { "input": { "text": "What is the main difference between the Dickey Fuller (DF) and Phillips-Perron (PP) approaches to unit root testing?" }, "references": [ { "output": { "text": "ADF is a single equation approach to unit root testing while PP is a systems approach" }, "tags": [] }, { "output": { "text": "PP tests reverse the DF null and alternative hypotheses so that there is stationarity under the null hypothesis of the PP test" }, "tags": [] }, { "output": { "text": "The PP test incorporates an automatic correction for autocorrelated residuals in the test regression" }, "tags": [ "correct" ] }, { "output": { "text": "PP tests have good power in small samples whereas DF tests do not." }, "tags": [] } ], "split": "test", "id": "id18" }, { "input": { "text": "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?" }, "references": [ { "output": { "text": "Residuals appear to be positively autocorrelated" }, "tags": [] }, { "output": { "text": "Residuals appear to be negatively autocorrelated" }, "tags": [] }, { "output": { "text": "Residuals appear not to be autocorrelated" }, "tags": [] }, { "output": { "text": "The test result is inconclusive" }, "tags": [ "correct" ] } ], "split": "test", "id": "id38" }, { "input": { "text": "Which of the following is a disadvantage of the fixed effects approach to estimating a panel model?" }, "references": [ { "output": { "text": "The model is likely to be technical to estimate" }, "tags": [] }, { "output": { "text": "The approach may not be valid if the composite error term is correlated with one or more of the explanatory variables" }, "tags": [] }, { "output": { "text": "The number of parameters to estimate may be large, resulting in a loss of degrees of freedom" }, "tags": [ "correct" ] }, { "output": { "text": "The fixed effects approach can only capture cross-sectional heterogeneity and not temporal variation in the dependent variable." }, "tags": [] } ], "split": "test", "id": "id80" }, { "input": { "text": "Negative residual autocorrelation is indicated by which one of the following?" }, "references": [ { "output": { "text": "A cyclical pattern in the residuals" }, "tags": [] }, { "output": { "text": "An alternating pattern in the residuals" }, "tags": [ "correct" ] }, { "output": { "text": "A complete randomness in the residuals" }, "tags": [] }, { "output": { "text": "Residuals that are all close to zero" }, "tags": [] } ], "split": "test", "id": "id31" }, { "input": { "text": "Which one of the following statements is true concerning alternative forecast accuracy measures?" }, "references": [ { "output": { "text": "Mean squared error is usually highly correlated with trading rule profitability" }, "tags": [] }, { "output": { "text": "Mean absolute error provides a quadratic loss function" }, "tags": [] }, { "output": { "text": "Mean absolute percentage error is a useful measure for evaluating asset return forecasts" }, "tags": [] }, { "output": { "text": "Mean squared error penalises large forecast errors disproportionately more than small forecast errors" }, "tags": [ "correct" ] } ], "split": "test", "id": "id125" }, { "input": { "text": "Which of the following statements is TRUE concerning the standard regression model?" }, "references": [ { "output": { "text": "y has a probability distribution" }, "tags": [ "correct" ] }, { "output": { "text": "x has a probability distribution" }, "tags": [] }, { "output": { "text": "The disturbance term is assumed to be correlated with x" }, "tags": [] }, { "output": { "text": "For an adequate model, the residual (u-hat) will be zero for all sample data points" }, "tags": [] } ], "split": "test", "id": "id67" }, { "input": { "text": "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" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id65" }, { "input": { "text": "Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?\n\ni) The acf and pacf will always be identical at lag one whatever the model\n\nii) The pacf for an MA(q) model will in general be non-zero beyond lag q\n\niii) The pacf for an AR(p) model will be zero beyond lag p\n\niv) The acf and pacf will be the same at lag two for an MA(1) model" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id130" }, { "input": { "text": "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?" }, "references": [ { "output": { "text": "All of them" }, "tags": [] }, { "output": { "text": "The largest 2" }, "tags": [] }, { "output": { "text": "The smallest 2" }, "tags": [ "correct" ] }, { "output": { "text": "The second largest" }, "tags": [] } ], "split": "test", "id": "id83" }, { "input": { "text": "An \"ex ante\" forecasting model is one which" }, "references": [ { "output": { "text": "Includes only contemporaneous values of variables on the RHS" }, "tags": [] }, { "output": { "text": "Includes only contemporaneous and previous values of variables on the RHS" }, "tags": [] }, { "output": { "text": "Includes only previous values of variables on the RHS" }, "tags": [ "correct" ] }, { "output": { "text": "Includes only contemporaneous values of exogenous variables on the RHS" }, "tags": [] } ], "split": "valid", "id": "id8" }, { "input": { "text": "If a relevant variable is omitted from a regression equation, the consequences would be that:\n\ni) The standard errors would be biased\n\n\nii) If the excluded variable is uncorrelated with all of the included variables, all of\n\nthe slope coefficients will be inconsistent.\n\n\niii) If the excluded variable is uncorrelated with all of the included variables, the\n\nintercept coefficient will be inconsistent.\n\n\niv) If the excluded variable is uncorrelated with all of the included variables, all of\n\nthe slope and intercept coefficients will be consistent and unbiased but inefficient." }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id71" }, { "input": { "text": "A leptokurtic distribution is one which" }, "references": [ { "output": { "text": "Has fatter tails and a smaller mean than a normal distribution with the same mean and variance" }, "tags": [] }, { "output": { "text": "Has fatter tails and is more peaked at the mean than a normal distribution with the same mean and variance" }, "tags": [ "correct" ] }, { "output": { "text": "Has thinner tails and is more peaked at the mean than a normal distribution with the same mean and variance" }, "tags": [] }, { "output": { "text": "Has thinner tails than a normal distribution and is skewed." }, "tags": [] } ], "split": "valid", "id": "id11" }, { "input": { "text": "Which of the following is a DISADVANTAGE of using pure time-series models (relative to structural models)?" }, "references": [ { "output": { "text": "They are not theoretically motivated" }, "tags": [ "correct" ] }, { "output": { "text": "They cannot produce forecasts easily" }, "tags": [] }, { "output": { "text": "They cannot be used for very high frequency data" }, "tags": [] }, { "output": { "text": "It is difficult to determine the appropriate explanatory variables for use in pure time-series models" }, "tags": [] } ], "split": "test", "id": "id64" }, { "input": { "text": "Which of the following could result in autocorrelated residuals?\n\ni) Slowness of response of the dependent variable to changes in the values of the independent variables\n\nii) Over-reactions of the dependent variable to changes in the independent variables\n\niii) Omission of relevant explanatory variables that are autocorrelated\n\niv) Outliers in the data" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id109" }, { "input": { "text": "Which one of the following is NOT a plausible remedy for near multicollinearity?" }, "references": [ { "output": { "text": "Use principal components analysis" }, "tags": [] }, { "output": { "text": "Drop one of the collinear variables" }, "tags": [] }, { "output": { "text": "Use a longer run of data" }, "tags": [] }, { "output": { "text": "Take logarithms of each of the variables" }, "tags": [ "correct" ] } ], "split": "test", "id": "id100" }, { "input": { "text": "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." }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id114" }, { "input": { "text": "If a series, y, follows a random walk with drift b, what is the optimal one-step ahead forecast of the change in y?" }, "references": [ { "output": { "text": "The current value of y" }, "tags": [] }, { "output": { "text": "Zero" }, "tags": [] }, { "output": { "text": "One" }, "tags": [] }, { "output": { "text": "The average value of the change in y over the in-sample period" }, "tags": [ "correct" ] } ], "split": "test", "id": "id56" }, { "input": { "text": "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" }, "references": [ { "output": { "text": "(i) only" }, "tags": [] }, { "output": { "text": "(i) and (ii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(ii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii)" }, "tags": [] } ], "split": "test", "id": "id128" }, { "input": { "text": "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." }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [ "correct" ] } ], "split": "test", "id": "id73" }, { "input": { "text": "Which of the following statements are true concerning a comparison between ARCH(q) and GARCH(1,1) models?\n\ni) The ARCH(q) model is likely to be the more parsimonious\n\n\nii) The ARCH(q) model is the more likely to violate non-negativity constraints\n\n\niii) The ARCH(q) model can allow for an infinite number of previous lags of squared\n\nreturns to affect the current conditional variance\n\n\niv) The GARCH(1,1) model will usually be sufficient to capture all of the dependence\n\nin the conditional variance" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i) and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), and (iii) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id32" }, { "input": { "text": "A parsimonious model is one that" }, "references": [ { "output": { "text": "Includes too many variables" }, "tags": [ "correct" ] }, { "output": { "text": "Includes as few variables as possible to explain the data" }, "tags": [] }, { "output": { "text": "Is a well-specified model" }, "tags": [] }, { "output": { "text": "Is a mis-specified model" }, "tags": [] } ], "split": "test", "id": "id23" }, { "input": { "text": "Which of the following statements are correct concerning the use of antithetic variates as part of a Monte Carlo experiment?\n\ni) Antithetic variates work by reducing the number of replications required to cover the whole probability space\n\nii) Antithetic variates involve employing a similar variable to that used in the simulation, but whose properties are known analytically\n\niii) Antithetic variates involve using the negative of each of the random draws and repeating the experiment using those values as the draws\n\niv) Antithetic variates involve taking one over each of the random draws and repeating the experiment using those values as the draws" }, "references": [ { "output": { "text": "(ii) and (iv) only" }, "tags": [] }, { "output": { "text": "(i) and (iii) only" }, "tags": [ "correct" ] }, { "output": { "text": "(i), (ii), and (iv) only" }, "tags": [] }, { "output": { "text": "(i), (ii), (iii), and (iv)" }, "tags": [] } ], "split": "test", "id": "id124" }, { "input": { "text": "If a threshold autoregressive (TAR) model is termed a \"SETAR\", what must be true about it?" }, "references": [ { "output": { "text": "It must follow a Markov process" }, "tags": [] }, { "output": { "text": "The model must contain only two regimes" }, "tags": [] }, { "output": { "text": "The state-determining variable must be the variable being modelled" }, "tags": [ "correct" ] }, { "output": { "text": "The number of lagged variables on the RHS of the equations for each regime must be the same" }, "tags": [] } ], "split": "valid", "id": "id16" } ]