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-ctional data and time ries data?
a. Cross-ctional data is bad on temporal ordering, whereas time ries data is not.
b. Time ries data is bad on temporal ordering, whereas cross ctional data is not.
c. Cross-ctional data consists of only qualitative variables, whereas time ries data consists of only quantitative variables.
d. Time ries data consists of only qualitative variables, whereas cross-ctional data does not include qualitative variables.
2. A stochastic process refers to a:
a. quence of random variables indexed by time.
b. quence of variables that can take fixed qualitative values.
有朋自远方来英语
c. quence of random variables that can take binary values only.
d. quence of random variables estimated at the same point of tim
slowly的比较级
ezioe.
英语4级听力下载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.
英语单词大全b. finite distributed lag model of order 1.
c. finite distributed lag model of order 2.id是什么意思啊
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 reprents:
a. the short-run change in y given a temporary increa in s.
b. the short-run change in y given a permanent increa in s.
c. the long-run change in y given a permanent increa in s.
d. the long-run change in y given a temporary increa in s.
6. Which of the following is an assumption on which time ries regression is bad?
a. A time ries process follows a model that is nonlinear in parameters.
pep三年级英语上册b. In a time ries process, no independent variable is a perfect linear combination of the others.
c. In a time ries 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 asonally adjusted ries is one which:
清华少儿英语加盟
a. has had asonal factors added to it.
b. has asonal factors removed from it.
c. has qualitative dependent variables reprenting different asons.
d. has qualitative explanatory variables reprenting different asons.
8. A process is stationary if:
a. any collection of random variables in a quence is taken and shifted ahead by h time periods; the joint probability distribution changes.
b. any collection of random variables in a quence is taken and shifted ahead by h time periods, the joint probability distribution remains unchanged.
c. there is rial correlation between the error terms of successive time periods and the explanatory variables and the error terms have positive covariance.
d. there is no rial 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 cond 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+?) 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+?) 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+?) 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+?) depends only on ‘t’ and not on ‘h’.
10. A covariance stationary time ries is weakly dependent if:
a. the correlation between the independent variable at time ‘t’ and the dependent variable at
enhancementtime ‘t + h’ goes to ∞ as h→0.
b. the correlation between the independent variable at time ‘t’ and the dependent variable at
工商行政管理局英文time ‘t + h’ goes to 0 as h →∞ .
c. the correlation between the independent variable at time ‘t’ and the independent variable
at time ‘t + h’ goes to 0 as h →∞ .
d. the correlation between the independent variable at time ‘t’ and the independent variable at time ‘t + h’ goes to ∞ as h →∞ .
11. The model yt = α1yt?1 +et, t =1,2,…. , where et is an i.i.d. quence with zero mean a
nd variance σe 2 reprents a(n):
