Chapter 10
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 sample size for a time series data set is the number of:
a. variables being measured.
b. time periods over which we observe the variables of interest less the number of variables being
measured.
c. time periods over which we observe the variables of interest plus the number of variables being
measured.
d. time periods over which we observe the variables of interest.
4. The model: Yt = β0 + β1ct + ut, 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.
5. A static model is postulated when:
a. a change in the independent variable at time ‘t’ is believed to have an effect on the dependent
variable at period ‘t + 1’.
b. a change in the independent variable at time ‘t’ is believed to have an effect on the dependent
variable for all successive time periods.
c. a change in the independent variable at time ‘t’ does not have any effect on the dependent variable.
d. a change in the independent variable at time ‘t’ is believed to have an immediate effect on the
dependent variable.
6. Refer to the following model.
yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut
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.
7. Refer to the following model.
yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut
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.
8. 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.
9. Under the assumptions of time series regression, which of the following statements will be true of the
following model: yt = α0 + α1dt + ut?
a. d can have a lagged effect on y.
b. ut can be correlated with past and future values of d.
c. Changes in the error term cannot cause future changes in d.
d. Changes in d cannot cause changes in y at the same point of time.
10. If an explanatory variable is strictly exogenous it implies that:
a. changes in the lag of the variable does not affect future values of the dependent variable.
b. the variable is correlated with the error term in all future time periods.
c. the variable cannot react to what has happened to the dependent variable in the past.
d. the conditional mean of the error term given the variable is zero.
11. A study which observes whether a particular occurrence influences some outcome is referred to as
a(n):
a. event study.
b. exponential study.
c. laboratory study.
d. comparative study.
12. With base year 1990, the index of industrial production for the year 1999 is 112. What will be the
value of the index in 1999, if the base year is changed to 1982 and the index measured 96 in 1982?
a. 112.24
b. 116.66
c. 85.71
d. 92.09
13. Which of the following statements is true?
a. The average of an exponential time series is a linear function of time.
b. The average of a linear sequence is an exponential function of time.
c. When a series has the same average growth rate from period to period, it can be approximated with
an exponential trend.
d. When a series has the same average growth rate from period to period, it can be approximated with a
linear trend.
14. Adding a time trend can make an explanatory variable more significant if:
a. the dependent and independent variables have similar kinds of trends, but movement in the
independent variable about its trend line causes movement in the dependent variable away from its
trend line.
b. the dependent and independent variables have similar kinds of trends and movement in the
independent variable about its trend line causes movement in the dependent variable towards its trend
line.
c. the dependent and independent variables have different kinds of trends and movement in the
independent variable about its trend line causes movement in the dependent variable towards its trend
line.
d. the dependent and independent variables have different kinds of trends, but movement in the
independent variable about its trend line causes movement in the dependent variable away from its
trend line.
15. 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 explanatory variables representing different seasons.
b. has qualitative dependent variables representing different seasons.
16. Economic time series are outcomes of random variables.
17. In a static model, one or more explanatory variables affect the dependent variable with a lag.
18. Time series regression is based on series which exhibit serial correlation.
19. Price indexes are necessary for turning a time series measured in real value into nominal value.
20. Dummy variables can be used to address the problem of seasonality in regression models.