Deck 10: Basic Regression Analysis With Time Series Data

Question 1

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.

Answer 1

D
Explanation: The sample size for a time series data set is the number of time periods over which we observe the variables of interest.

Question 2

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.

Answer 2

C
Explanation: If an explanatory variable is strictly exogenous it implies that the variable cannot react to what has happened to the dependent variable in the past.

Question 3

Economic time series are outcomes of random variables.

Answer 3

True
Explanation: Economic time series are outcomes of random variables.

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

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 6

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.

Question 7

Refer to the following model y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_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 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 9

​Which of the following rules out perfect collinearity among the regressors?

A)​​Multiple regression
B)​Simple regression
C) ​Time series regression
D) ​Cross-sectional regression

Question 10

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.

Question 11

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

Question 12

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 13

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 14

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.

Question 15

The model: Y_t = ₀ + ₁c_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.

Question 16

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 17

If ₁ > 0, then y_{t ​}in the linear function of time E(y_t) = ₀ + ₁t displays a(n):​

A)​upward trend.
B)​downward trend.
C) exponential trend.
D) quadratic trend.

Question 18

The propensity δ₀ + δ₁+ … + δ_k is sometimes called the:​

A)​short-run elasticity, which measures the percentage increase in a dependent variable after k quarters given a permanent 1% increase in the k independent variables.
B)​long-run elasticity, which measures the percentage increase in a dependent variable after k quarters given a permanent 1% increase in the k independent variables.
C) ​short-run elasticity, which measures the percentage decrease in a dependent variable after k quarters given a permanent 1% decrease in the k independent variables.
D) ​​long-run elasticity, which measures the percentage decrease in a dependent variable after k quarters given a permanent 1% decrease in the k independent variables.

Question 19

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

Question 20

Refer to the following model. y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_t-3 + u_{t 0} + ₁ + ₂ + ₃ 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.

Question 21

​When a series has the same average growth rate from period to period, then it can be approximated by an exponential trend.

Question 22

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

Question 23

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

Question 24

The short-run elasticity measures the immediate percentage change in a dependent variable given a 1% increase in the independent variables.​