Question 1

Economic time series are outcomes of random variables.

Accepted Answer

A) True 
 B)False  True

Question 2

The model: Yt =   0 +   1ct + ut, t = 1,2,……., n is an example of a(n):

Accepted Answer

A) autoregressive conditional heteroskedasticity model. 
B) static model. 
C)  finite distributed lag model. 
D)  infinite distributed lag model. 
A) autoregressive conditional heteroskedasticity model. 
B) static model. 
C)  finite distributed lag model. 
D)  infinite distributed lag model. B

Question 3

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?

Accepted Answer

A) 112.24 
B) 116.66 
C)  85.71 
D)  92.09 
A) 112.24 
B) 116.66 
C)  85.71 
D)  92.09 B

Question 4

If an explanatory variable is strictly exogenous it implies that:

Accepted Answer

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. 
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 5

Adding a time trend can make an explanatory variable more significant if:

Accepted Answer

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. 
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 6

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

Accepted Answer

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

Question 7

Which of the following is an assumption on which time series regression is based?

Accepted Answer

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. 
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.

Question 8

A seasonally adjusted series is one which:

Accepted Answer

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. 
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 9

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

Accepted Answer

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

Question 10

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

Accepted Answer

A) True 
 B)False

Question 11

A stochastic process refers to a:

Accepted Answer

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. 
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 12

Which of the following correctly identifies a difference between cross-sectional data and time series data?

Accepted Answer

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. 
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

A static model is postulated when:

Accepted Answer

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. 
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 14

Refer to the following model. yt =   0 +   0st +   1st-1 +   2st-2 +   3st-3 + ut   0 +   1 +   2 +   3 represents:

Accepted Answer

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. 
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 15

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

Accepted Answer

A) True 
 B)False

Question 16

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

Accepted Answer

A) True 
 B)False

Question 17

The sample size for a time series data set is the number of:

Accepted Answer

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. 
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 18

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

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

A) True 
 B)False

Question 20

Refer to the following model yt =   0 +   0st +   1st-1 +   2st-2 +   3st-3 + ut
This is an example of a(n):

Accepted Answer

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. 
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.

Economic time series are outcomes of random variables.

The model: Y_t = ₀ + ₁c_t + u_t, t = 1,2,……., n is an example of a(n):

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?

If an explanatory variable is strictly exogenous it implies that:

Adding a time trend can make an explanatory variable more significant if:

If ₁ > 0, then y_tin the linear function of time E(y_t) = ₀ + ₁t displays a(n):

Which of the following is an assumption on which time series regression is based?

A seasonally adjusted series is one which:

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

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

A stochastic process refers to a:

Which of the following correctly identifies a difference between cross-sectional data and time series data?

A static model is postulated when:

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:

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

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

The sample size for a time series data set is the number of:

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

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

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):

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 10: Basic Regression Analysis With Time Series Data

Economic time series are outcomes of random variables.

The model: Yt = 0 + 1ct + ut, t = 1,2,……., n is an example of a(n):

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?

If an explanatory variable is strictly exogenous it implies that:

Adding a time trend can make an explanatory variable more significant if:

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

Which of the following is an assumption on which time series regression is based?

A seasonally adjusted series is one which:

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

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

A stochastic process refers to a:

Which of the following correctly identifies a difference between cross-sectional data and time series data?

A static model is postulated when:

Refer to the following model. yt = 0 + 0st + 1st-1 + 2st-2 + 3st-3 + ut 0 + 1 + 2 + 3 represents:

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

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

The sample size for a time series data set is the number of:

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

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

Refer to the following model yt = 0 + 0st + 1st-1 + 2st-2 + 3st-3 + ut This is an example of a(n):

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

The model: Y_t = ₀ + ₁c_t + u_t, t = 1,2,……., n is an example of a(n):

If ₁ > 0, then y_tin the linear function of time E(y_t) = ₀ + ₁t displays a(n):

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

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

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:

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):