2019年高级计量经济学考试(共5页).docx

精选优质文档-倾情为你奉上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 time.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 +𝛽1𝑠𝑡1 +𝛽2𝑠𝑡2 +𝛽3𝑠𝑡3 +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 +𝛽1𝑠𝑡1 +𝛽2𝑠𝑡2 +𝛽3𝑠𝑡3 +𝜇𝑡 𝛽0+ 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 positive.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 covariance.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+) 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 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 h0. 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 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 = 𝛼1𝑦𝑡1 +𝑒𝑡, t =1,2,. , where 𝑒𝑡 is an i.i.d. sequence with zero mean and variance 𝜎𝑒 2 represents a(n): a. moving average process of order one. b. moving average process of order two. c. autoregressive process of order one. d. autoregressive process of order two.12. Which of the following is assumed in time series regression? a. There is no perfect collinearity between the explanatory variables. b. The explanatory variables are contemporaneously endogenous. c. The error terms are contemporaneously heteroskedastic. d. The explanatory variables cannot have temporal ordering.13. Consider the model: 𝑦𝑡 = 𝛽0 +𝛽1𝑧1𝑡 +𝛽2𝑧2𝑡 +𝜇𝑡. Under weak dependence, the condition sufficient for consistency of OLS is: a. E(zt1|zt2) = 0. b. E(yt |zt1, zt2) = 0. c. E(ut |zt1, zt2) = 0. d. E(ut |zt1, zt2) = .14. The model 𝑦𝑡 = 𝑦𝑡1+et, t = 1, 2, represents a: a. AR(2) process. b. MA(1) process. c. random walk process. d. random walk with a drift process.15. Which of the following statements is true? a. A random walk process is stationary. b. The variance of a random walk process increases as a linear function of time. c. Adding a drift term to a random walk process makes it stationary. d. The variance of a random walk process with a drift decreases as an exponential function of time.16. If a process is said to be integrated of order one, or I(1), _. a. it is stationary at level b. averages of such processes already satisfy the standard limit theorems c. the first difference of the process is weakly dependent d. it does not have a unit root17. In the presence of serial correlation: a. estimated standard errors remain valid. b. estimated test statistics remain valid.c. estimated OLS values are not BLUE. d. estimated variance does not differ from the case of no serial correlation.18. When a series is stationary, weakly dependent, and has serial correlation: a. the adjusted 𝑅2 is inconsistent, while 𝑅2 is a consistent estimator of the population parameter. b. the adjusted 𝑅2 is consistent, while 𝑅2 is an inconsistent estimator of the population parameter. c. both the adjusted 𝑅2 and 𝑅2 are inconsistent estimators of the population parameter. d. both the adjusted 𝑅2 and 𝑅2 are consistent estimators of the population parameter.19. A smaller standard error means: a. a larger t statistic. b. a smaller t statistic. c. a larger F statistic. d. a smaller F statistic.20. In a model based on a weakly dependent time series with serial correlation and strictly exogenous explanatory variables, _. a. the feasible generalized least square estimates are unbiased b. the feasible generalized least square estimates are BLUE c. the feasible generalized least square estimates are asymptotically more efficient than OLS estimates d. the feasible generalized least square estimates are asymptotically less efficient than OLS estimates21. Which of the following identifies an advantage of first differencing a time-series? a. First differencing eliminates most of the serial correlation. b. First differencing eliminates most of the heteroskedastcicty. c. First differencing eliminates most of the multicollinearity. d. First differencing eliminates the possibility of spurious regression.22. Which of the following is a limitation of serial correlation robust standard errors? a. The serial correlation-robust standard errors are smaller than OLS standard errors when there is serial correlation. b. The serial correlation-robust standard errors can be poorly behaved when there is substantial serial correlation and the sample size is small. c. The serial correlation-robust standard errors cannot be calculated for autoregressive processes of an order greater than one. d. The serial correlation-robust standard errors cannot be calculated after relaxing the assumption of homoskedasticity.23. In the time series literature, the serial correlationrobust standard errors are sometimes called: a. homoskedasticity and autocorrelation inconsistent standard errors. b. homoskedasticity and autocorrelation consistent standard errors. c. heteroskedasticity and autocorrelation inconsistent standard errors. d. heteroskedasticity and autocorrelation consistent standard errors.24. In the presence of heteroskedasticity, the usual OLS estimates of: a. standard errors are valid, whereas the t statistics and F statistics are invalid. b. t statistics are valid, but the standard errors and F statistics are invalid. c. F statistics are valid, but the standard errors and t statistics are invalid. d. standard errors, t statistics, and F statistics are invalid.25. Which of the following tests can be used to test for heteroskedasticity in a time series? a. Johansen test b. Dickey-Fuller test c. Breusch-Pagan test d. Durbins alternative test二、请解释菲利普斯曲线，并说明在计量经济学中的应用（5分）三、请列举时间序列经典假设CLM（5分）四、运用有限分布滞后模型或其他可行模型，建立模型分析说明二孩政策对生育率影响（10分）五、结合讨论过的一个例子，列举并分析一个时间序列经典模型（10分）六、结合讨论过的一个例子，列举并分析一个面板数据模型（10分）七、结合自己研究或学习的一个例子，说明经验研究分析的主要步骤（10分）专心-专注-专业