2019年高级计量经济学考试

高级计量经济学考试

一、单选题（25 *2分）

1. Which of the following correctly identifies a difference between cross-sectional data and time series data?

a. Cross-sectional data is based on temporal ordering, whereas time series data is not.

b. Time series data is based on temporal ordering, whereas cross sectional data is not.

c. Cross-sectional data consists of only qualitative variables, whereas time series data consists of only quantitative variables.

d. Time series data consists of only qualitative variables, whereas cross-sectional data does not include qualitative variables.

2. A stochastic process refers to a:

a. sequence of random variables indexed by time.

b. sequence of variables that can take fixed qualitative values.

c. sequence of random variables that can take binary values only.

d. sequence of random variables estimated at the same point of tim

e.

3. The model: yt = β0 +β1ct +μ , t = 1,2,……., n is an example of a(n):

a. Autoregressive conditional heteroskedasticity model.

b. static model.

c. finite distributed lag model.

d. infinite distributed lag model.

4. Refer to the following model yt = α0 +β0st +β1st−1 +β2st−2 +β3st−3 +μt This is an example of a(n):

a. infinite distributed lag model.

b. finite distributed lag model of order 1.

c. finite distributed lag model of order 2.

d. finite distributed lag model of order 3.

5. Refer to the following model. yt = α0 +β0st +β1st−1 +β2st−2 +β3st−3 +μtβ0+ β1 + β2 + β3 represents:

a. the short-run change in y given a temporary increase in s.

b. the short-run change in y given a permanent increase in s.

c. the long-run change in y given a permanent increase in s.

d. the long-run change in y given a temporary increase in s.

6. Which of the following is an assumption on which time series regression is based?

a. A time series process follows a model that is nonlinear in parameters.

b. In a time series process, no independent variable is a perfect linear combination of the others.

c. In a time series process, at least one independent variable is a constant.

d. For each time period, the expected value of the error ut, given the explanatory variables for all time periods, is positiv

e.

7. A seasonally adjusted series is one which:

a. has had seasonal factors added to it.

b. has seasonal factors removed from it.

c. has qualitative dependent variables representing different seasons.

d. has qualitative explanatory variables representing different seasons.

8. A process is stationary if:

a. any collection of random variables in a sequence is taken and shifted ahead by h time periods; the joint probability distribution changes.

b. any collection of random variables in a sequence is taken and shifted ahead by h time periods, the joint probability distribution remains unchanged.

c. there is serial correlation between the error terms of successive time periods and the explanatory variables and the error terms have positive covariance.

d. there is no serial correlation between the error terms of successive time periods and the explanatory variables and the error terms have positive covarianc

e.

9. A stochastic process {xt: t = 1,2,….} with a finite second moment [E(xt 2) < ∞ ] is covariance stationary if:

a. E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+ℎ) depends only on ‘h’ and not on ‘t’.

b. E(xt) is variable, Var(xt) is variable, and for any t, h≥ 1, Cov(xt, xt+ℎ) depends only on ‘t’ and not on h.

c. E(xt) is constant, Var(xt) is constant, and for any t, h ≥1, Cov(xt, xt+ℎ) depends only on ‘h’ and not on ‘t’.

d. E(xt) is constant, Var(xt) is constant, and for any t, h ≥1, Cov(xt, xt+ℎ) depends only on ‘t’ and not on ‘h’.

10. A covariance stationary time series is weakly dependent if:

a. the correlation between the independent variable at time ‘t’ and the dependent variable at

time ‘t + h’ goes to ∞ as h→0.

b. the correlation between the independent variable at time ‘t’ and the dependent variable at

time ‘t + h’ goes to 0 as h →∞ .

c. the correlation between the independent variable at time ‘t’ and the independent variable