Deck 10: Basic Regression Analysis With Time Series Data

Question 1

Dummy variables can be used to address the problem of seasonality in regression models.

Answer 1

True

Question 2

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.

Answer 2

D

Question 3

The model: Y_t = β₀ + β₁c_t + u_t,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.

Answer 3

B

Question 4

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.

Question 5

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t
Β₀ + β₁ + β₂ + β₃represents:

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.

Question 6

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.

Question 7

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t
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.

Question 8

A seasonally adjusted series is one which:

A)has had seasonal factors added to it.
B)has qualitative dependent variables representing different seasons.
B)has seasonal factors removed from it.
C)has qualitative explanatory variables representing different seasons.

Question 9

In a static model,one or more explanatory variables affect the dependent variable with a lag.

Question 10

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.

Question 11

Economic time series are outcomes of random variables.

Question 12

Price indexes are necessary for turning a time series measured in real value into nominal value.

Question 13

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.

Question 14

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.

Question 15

Time series regression is based on series which exhibit serial correlation.

Question 16

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.

Question 17

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.

Question 18

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

Question 19

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 u_t,given the explanatory variables for all time periods,is positive.