Econometrics

أسئلة الاختبار

1. 1. Which one of the following is the most appropriate definition of a 99% confidence interval?
   1. 99% of the time in repeated samples, the interval would contain the true value of the parameter
   2. 99% of the time in repeated samples, the interval would contain the estimated value of the parameter
   3. 99% of the time in repeated samples, the null hypothesis will be rejected
   4. 99% of the time in repeated samples, the null hypothesis will not be rejected when it was false
2. 2. What is the main difference between the Dickey Fuller (DF) and Phillips-Perron (PP) approaches to unit root testing?
   1. ADF is a single equation approach to unit root testing while PP is a systems approach
   2. PP tests reverse the DF null and alternative hypotheses so that there is stationarity under the null hypothesis of the PP test
   3. The PP test incorporates an automatic correction for autocorrelated residuals in the test regression
   4. PP tests have good power in small samples whereas DF tests do not.
3. 3. 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?
   1. It would rise more quickly for negative disturbances than for positive ones of the same magnitude
   2. It would be symmetrical about zero
   3. It would rise less quickly for negative disturbances than for positive ones of the same magnitude
   4. It would be zero for all positive disturbances
4. 4. Which of the following statements is false concerning the linear probability model?
   1. There is nothing in the model to ensure that the estimated probabilities lie between zero and one
   2. 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
   3. The error terms will be heteroscedastic and not normally distributed
   4. The model is much harder to estimate than a standard regression model with a continuous dependent variable
5. 5. Which of the following statements concerning the regression population and sample is FALSE?
   1. The population is the total collection of all items of interest
   2. The population can be infinite
   3. In theory, the sample could be larger than the population
   4. A random sample is one where each individual item from the population is equally likely to be drawn.
6. 6. 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?
   1. Asymptotically, the values of the two test statistics will be equal
   2. The Q test has better small-sample properties than the Q*
   3. The Q test is sometimes over-sized for small samples
   4. As the sample size tends towards infinity, both tests will show a tendency to always reject the null hypothesis of zero autocorrelation coefficients.
7. 7. A parsimonious model is one that
   1. Includes too many variables
   2. Includes as few variables as possible to explain the data
   3. Is a well-specified model
   4. Is a mis-specified model
8. 8. Which of the following is NOT a feature of continuously compounded returns (i.e. log-returns)?
   1. They can be interpreted as continuously compounded changes in the prices
   2. They can be added over time to give returns for longer time periods
   3. They can be added across a portfolio of assets to give portfolio returns
   4. They are usually fat-tailed
9. 9. Which of the following features of financial asset return time-series could be captured using a standard GARCH(1,1) model?
   
   i) Fat tails in the return distribution
   
   
   ii) Leverage effects
   
   
   iii) Volatility clustering
   
   
   iv) Volatility affecting returns
   1. (ii) and (iv) only
   2. (i) and (iii) only
   3. (i), (ii), and (iii) only
   4. (i), (ii), (iii), and (iv)
10. 10. 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?
    1. Less than -1
    2. Between -1 and 0
    3. Between 0 and 1
    4. Bigger than 1
11. 11. Under which of the following situations would bootstrapping be preferred to pure simulation?
    
    i) If it is desired that the distributional properties of the data in the experiment
    
    are the same as those of some actual data
    
    
    ii) If it is desired that the distributional properties of the data in the experiment
    
    are known exactly
    
    
    iii) If the distributional properties of the actual data are unknown
    
    
    iv) If the sample of actual data available is very small
    1. (ii) and (iv) only
    2. (i) and (iii) only
    3. (i), (ii), and (iv) only
    4. (i), (ii), (iii), and (iv)
12. 12. Which of the following may be consequences of one or more of the CLRM assumptions being violated?
    
    i) The coefficient estimates are not optimal
    
    
    ii) The standard error estimates are not optimal
    
    
    iii) The distributions assumed for the test statistics are inappropriate
    
    
    iv) Conclusions regarding the strength of relationships between the dependent
    
    and independent variables may be invalid.
    1. (ii) and (iv) only
    2. (i) and (iii) only
    3. (i), (ii), and (iii) only
    4. (i), (ii), (iii), and (iv)
13. 13. Which of the following statements is true concerning forecasting in econometrics?
    1. Forecasts can only be made for time-series data
    2. Mis-specified models are certain to produce inaccurate forecasts
    3. Structural forecasts are simpler to produce than those from time series models
    4. In-sample forecasting ability is a poor test of model adequacy
14. 14. The pacf is necessary for distinguishing between
    1. An AR and an MA model
    2. An AR and an ARMA model
    3. An MA and an ARMA model
    4. Different models from within the ARMA family
15. 15. Negative residual autocorrelation is indicated by which one of the following?
    1. A cyclical pattern in the residuals
    2. An alternating pattern in the residuals
    3. A complete randomness in the residuals
    4. Residuals that are all close to zero
16. 16. Which of the following statements are true concerning a comparison between ARCH(q) and GARCH(1,1) models?
    
    i) The ARCH(q) model is likely to be the more parsimonious
    
    
    ii) The ARCH(q) model is the more likely to violate non-negativity constraints
    
    
    iii) The ARCH(q) model can allow for an infinite number of previous lags of squared
    
    returns to affect the current conditional variance
    
    
    iv) The GARCH(1,1) model will usually be sufficient to capture all of the dependence
    
    in the conditional variance
    1. (ii) and (iv) only
    2. (i) and (iii) only
    3. (i), (ii), and (iii) only
    4. (i), (ii), (iii), and (iv)
17. 17. Which of the following statements are true concerning a triangular or recursive system?
    
    i) The parameters can be validly estimated using separate applications of OLS to
    
    each equation
    
    
    ii) The independent variables may be correlated with the error terms in other
    
    equations
    
    
    iii) An application of 2SLS would lead to unbiased but inefficient parameter estimates
    
    
    iv) The independent variables may be correlated with the error terms in the equations
    
    in which they appear as independent variables
    1. (ii) and (iv) only
    2. (i) and (iii) only
    3. (i), (ii), and (iii) only
    4. (i), (ii), (iii), and (iv)
18. 18. Which one of the following statements best describes the algebraic representation of the fitted regression line?
    1. \hat{y}_t = \hat{\alpha} + \hat{\beta}x_t + \hat{u}_t
    2. \hat{y}_t = \hat{\alpha} + \hat{\beta}x_t
    3. \hat{y}_t = \hat{\alpha} + \hat{\beta}x_t + u_t
    4. y_t = \hat{\alpha} + \hat{\beta}x_t + \hat{u}_t
19. 19. What are the dimensions of $\hat{u}^t \hat{u}?
    1. T x k
    2. T x 1
    3. k x 1
    4. 1 x 1
20. 20. The characteristic roots of the MA process
    
    $y_t = -3u_{t-1} + u_{t-2} + u_t$
    
    are
    1. 1 and 2
    2. 1 and 0.5
    3. 2 and -0.5
    4. 1 and -3
21. 21. Which of the following is an equivalent expression for saying that the explanatory variable is "non-stochastic"?
    1. The explanatory variable is partly random
    2. The explanatory variable is fixed in repeated samples
    3. The explanatory variable is correlated with the errors
    4. The explanatory variable always has a value of one
22. 22. 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?
    1. Residuals appear to be positively autocorrelated
    2. Residuals appear to be negatively autocorrelated
    3. Residuals appear not to be autocorrelated
    4. The test result is inconclusive
23. 23. If OLS is used in the presence of autocorrelation, which of the following will be likely consequences?
    
    i) Coefficient estimates may be misleading
    
    
    ii) Hypothesis tests could reach the wrong conclusions
    
    
    iii) Forecasts made from the model could be biased
    
    
    iv) Standard errors may inappropriate
    1. (ii) and (iv) only
    2. (i) and (iii) only
    3. (i), (ii), and (iii) only
    4. (i), (ii), (iii), and (iv)
24. 24. What will be the properties of the OLS estimator in the presence of multicollinearity?
    1. It will be consistent, unbiased and efficient
    2. It will be consistent and unbiased but not efficient
    3. It will be consistent but not unbiased
    4. It will not be consistent
25. 25. Which one of the following would NOT be a consequence of using non-stationary data in levels form?
    1. The regression $R^2$ may be spuriously high
    2. Test statistics may not follow standard distributions
    3. Statistical inferences may be invalid
    4. Parameter estimates may be biased