计量经济学Test-bank-questions-Chapter-5

1、Multiple Choice Test Bank Questi ons No Feedback -Chapter 5Correct answers denoted by an asterisk.1. Consider the following model estimated for a time series yt = 0.3 + 0.5 yt-1 - 0.4 t-1 + twhere t is a zero mean error process.What is the (unconditional) mean of the seriesy, t ?(a) * 0.6(b) 0.3(c)

2、0.0(d) 0.42. Consider the following single exponential smoothing model:St = Xt + (1- ) St-1You are given the following data:?=0.1, Xt=0.5,St-1=0.2If we believe that the true DGP can be approximated by the exponential smoothing model, what would be an appropriate 2-step ahead forecast foXr ? (i.e. a

3、forecast of Xt+2 made at time t)(a) 0.2(b) * 0.23(c) 0.5(d) There is insufficient information given in the question to form more than a one step ahead forecast.3. Consider the following MA(3) process.yt = 0.1 + 0.4ut-1 + 0.2ut-2 -0.1ut-3 + utWhat is the optimal forecast for yt, 3 steps into the futu

4、re (i.e. for time t+2 if all information until time t-1 is available), if you have the following data?ut-1 = 0.3; ut-2 = -0.6; ut-3 = -0.3(a) 0.4(b) 0.0(c) * 0.07(d) -0.14. Which of the following sets of characteristics would usually best describe an autoregressive process of order 3 (i.e. an AR(3)?

5、(a) * A slowly decaying acf, and a pacf with 3 significant spikes(b) A slowly decaying pacf and an acf with 3 significant spikes(c) A slowly decaying acf and pacf(d) An acf and a pacf with 3 significant spikes5. A process, xt, which has a constant mean and variance, and zero autocovariance for all n

6、on-zero lags is best described as(a) * A white noise process(b) A covariance stationary process(c) An autocorrelated process(d) A moving average process6. Which of the following conditions must hold for the autoregressive part of an ARMA model to be stationary?(a) * All roots of the characteristic e

7、quation must lie outside the unit circle(b) All roots of the characteristic equation must lie inside the unit circle(c) All roots must be smaller than unity(d) At least one of the roots must be bigger than one in absolute value.7. Which of the following statements are true concerning time-series for

8、ecasting?(i) All time-series forecasting methods are essentially extrapolative.(ii) Forecasting models are prone to perform poorly following a structural break in a series.(iii) Forecasting accuracy often declines with prediction horizon.(iv) The mean squared errors of forecasts are usually very hig

9、hly correlated with the profitability of employing those forecasts in a trading strategy.(a) (i), (ii), (iii), and (iv)(b) * (i), (ii) and (iii) only(c) (ii), (iii) only(d) (ii) and (iv) only8. If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for y?(a

10、) * The current value of y.(b) Zero.(c) The historical unweighted average ofy.(d) An exponentially weighted average of previous values ofy.9. 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 autocorrelation function at lag 1?(a)

11、 0.4(b) 1(c) *0.34(d) It is not possible to determine the value of the autocovariances without knowing the disturbance variance.10. Which of the following statements are true?(i) An MA(q) can be expressed as an AR(infinity) if it is invertible(ii) An AR(p) can be writte n as an MA(i nfini ty) if it

12、is stati onary(iii) The (uncon diti on al) mean of an ARMA process will depe nd only on the in tercept and on the AR coefficie nts and not on the MA coefficie nts(iv) A ran dom walk series will have zero pacf except at lag 1(a) (ii) and (iv) only(b) (i) and (iii) only(c) (i), (ii), and (iii) only(d)

13、 * (i), (ii), (iii), and (iv).11. Con sider the followi ng picture and suggest the model from the followi ng list that best characterises the process:fc apdnafca0.90.87 n-6 n-5 4 a a3 2 10 1-a a a a-(a) An AR(1)(b) An AR(2)(c) * An ARMA(1,1)(d) An MA(3)The acf is clearly declining very slowly in thi

14、s case, which is consistent with their being an autoregressive part to the appropriate model. The pacf is clearly significant for lags one and two, but the questi on is does it them become in sig nifica nt for lags 2 and 4, in dicati ng an AR(2) process, or does it rema in sig nifica nt, which would

15、 be more con siste nt with a mixed ARMA process? Well, give n the huge size of the sample that gave rise to this acf and pacf, even a pacf value of 0.001 would still be statistically sig nifica nt. Thus an ARMA process is the most likely can didate, although note that it would not be possible to tel

16、l from the acf and pacf which model from the ARMA family was more appropriate. The DGP for the data that gen erated this plot was y_t = 0.9 y_(t-1) -0.3 u (t-1) + u t.12. Which of the following models can be estimated using ordinary least squares?(i) An AR(1)(ii) An ARMA(2,0)(iii) An MA(1)(iv) An AR

17、MA(1,1)(a) (i) only(b) * (i) and (ii) only(c) (i), (ii), and (iii) only(d) (i), (ii), (iii), and (iv).13. If a series, y, is described as-re“vemrtienagn ” , which model from the following list islikely to produce the best long-term forecasts for that series y?(a) A random walk(b) * The long term mea

18、n of the series(c) A model from the ARMA family(d) A random walk with drift14. Consider the following AR(2) model. What is the optimal 2-step ahead forecast for y if all information available is up to and including time t, if the values of y at time t, t-1 and t-2 are -0.3, 0.4 and -).1 respectively, and the value of u at time t-1 is 0.3?yt = -0.1 + 0.75yt-1 - 0.125yt-2 + ut(a) -0.1(b) 0.27(c) * -0.34(d) 0.3015. What is the optimal three-s