Question 1

Covariance stationarity focuses only on the first two moments of a stochastic process.

Accepted Answer

A) True 
 B)False  True

Question 2

Which of the following is assumed in time series regression?

Accepted Answer

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. 
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. A

Question 3

The variance of a random walk process decreases as a linear function of time.​

Accepted Answer

A) True 
 B)False  False

Question 4

Unit root processes, such as a random walk (with or without drift), are said to be:​

Accepted Answer

A) ​integrated of order one. 
B) ​integrated of order two. 
C) ​sequentially exogenous. 
D) ​asymptotically uncorrelated. 
A) ​integrated of order one. 
B) ​integrated of order two. 
C) ​sequentially exogenous. 
D) ​asymptotically uncorrelated.

Question 5

The model yt = yt - 1 + et, t = 1, 2, … represents a:

Accepted Answer

A) AR(2) process. 
B) MA(1) process. 
C) random walk process. 
D) random walk with a drift process. 
A) AR(2) process. 
B) MA(1) process. 
C) random walk process. 
D) random walk with a drift process.

Question 6

Suppose ut is the error term for time period 't' in a time series regression model the explanatory variables are xt = (xt1, xt2 …., xtk). The assumption that the errors are contemporaneously homoskedastic implies that:

Accepted Answer

Question 7

The homoskedasticity assumption in time series regression suggests that the variance of the error term cannot be a function of time.

Accepted Answer

A) True 
 B)False

Question 8

Which of the following statements is true?

Accepted Answer

A) A model with a lagged dependent variable cannot satisfy the strict exogeneity assumption. 
B) Stationarity is critical for OLS to have its standard asymptotic properties. 
C) Efficient static models can be estimated for nonstationary time series. 
D) In an autoregressive model, the dependent variable in the current time period varies with the error term of previous time periods. 
A) A model with a lagged dependent variable cannot satisfy the strict exogeneity assumption. 
B) Stationarity is critical for OLS to have its standard asymptotic properties. 
C) Efficient static models can be estimated for nonstationary time series. 
D) In an autoregressive model, the dependent variable in the current time period varies with the error term of previous time periods.

Question 9

Which of the following is a strong assumption for static and finite distributed lag models?​

Accepted Answer

A) ​Sequential exogeneity 
B) Strict exogeneity 
C) ​Dynamic completeness 
D) Homoskedasticity 
A) ​Sequential exogeneity 
B) Strict exogeneity 
C) ​Dynamic completeness 
D) Homoskedasticity

Question 10

If a process is said to be integrated of order one, or I(1), _____.

Accepted Answer

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 root 
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 root

Question 11

A stochastic process {xt: t = 1,2,….} with a finite second moment [E(xt2) <   ] is covariance stationary if:

Accepted Answer

A) E(xt) is variable, Var(xt) is variable, and for any t, h   1, Cov(xt, xt+h) 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+h) 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+h) 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+h) depends only on 't' and not on 'h'. 
A) E(xt) is variable, Var(xt) is variable, and for any t, h   1, Cov(xt, xt+h) 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+h) 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+h) 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+h) depends only on 't' and not on 'h'.

Question 12

A process is stationary if:

Accepted Answer

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. 
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.

Question 13

The first difference of an I(1) time series is weakly dependent.

Accepted Answer

A) True 
 B)False

Question 14

The model xt =   1xt - 1 + et, t =1,2,…. , where et is an i.i.d. sequence with zero mean and variance   2e represents a(n):

Accepted Answer

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. 
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.

Question 15

A covariance stationary time series is weakly dependent if:

Accepted Answer

A) the correlation between the independent variable at time 't' and the dependent variable at time 't + h' goes to   as h   0. 
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     . 
A) the correlation between the independent variable at time 't' and the dependent variable at time 't + h' goes to   as h   0. 
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     .

Question 16

If adding one more lag of the dependent variable would explain the dependent variable better, then the model is not dynamically complete.

Accepted Answer

A) True 
 B)False

Question 17

Which of the following is true if yt =   +   +   +   + ut is a dynamically complete model?

Accepted Answer

Question 18

Under adaptive expectations, the expected current value of a variable does not depend on a recently observed value of the variable.

Accepted Answer

A) True 
 B)False

Question 19

Weakly dependent processes are said to be integrated of order zero.

Accepted Answer

A) True 
 B)False

Question 20

Which of the following statements is true?

Accepted Answer

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. 
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.

Covariance stationarity focuses only on the first two moments of a stochastic process.

Which of the following is assumed in time series regression?

The variance of a random walk process decreases as a linear function of time.

Unit root processes, such as a random walk (with or without drift), are said to be:

The model y_t = y_{t -} ₁ + e_t, t = 1, 2, … represents a:

Suppose u_t is the error term for time period 't' in a time series regression model the explanatory variables are x_t = (x_t₁, x_t₂ …., x_tk). The assumption that the errors are contemporaneously homoskedastic implies that:

The homoskedasticity assumption in time series regression suggests that the variance of the error term cannot be a function of time.

Which of the following statements is true?

Which of the following is a strong assumption for static and finite distributed lag models?

If a process is said to be integrated of order one, or I(1), _____.

A stochastic process {x_t: t = 1,2,….} with a finite second moment [E(x_t²) < ] is covariance stationary if:

A process is stationary if:

The first difference of an I(1) time series is weakly dependent.

The model x_t = ₁x_{t -} ₁+ e_t, t =1,2,…. , where e_t is an i.i.d. sequence with zero mean and variance ²e represents a(n):

A covariance stationary time series is weakly dependent if:

If adding one more lag of the dependent variable would explain the dependent variable better, then the model is not dynamically complete.

Which of the following is true if y_t = + + + + u_t is a dynamically complete model?

Under adaptive expectations, the expected current value of a variable does not depend on a recently observed value of the variable.

Weakly dependent processes are said to be integrated of order zero.

Which of the following statements is true?

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

Heteroskedasticity

More on Specification and Data Issues

Basic Regression Analysis 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 Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 11: Further Issues in Using Ols With Time Sries Data

Covariance stationarity focuses only on the first two moments of a stochastic process.

Which of the following is assumed in time series regression?

The variance of a random walk process decreases as a linear function of time.​

Unit root processes, such as a random walk (with or without drift), are said to be:​

The model yt = yt - 1 + et, t = 1, 2, … represents a:

Suppose ut is the error term for time period 't' in a time series regression model the explanatory variables are xt = (xt1, xt2 …., xtk). The assumption that the errors are contemporaneously homoskedastic implies that:

The homoskedasticity assumption in time series regression suggests that the variance of the error term cannot be a function of time.

Which of the following statements is true?

Which of the following is a strong assumption for static and finite distributed lag models?​

If a process is said to be integrated of order one, or I(1), _____.

A stochastic process {xt: t = 1,2,….} with a finite second moment [E(xt2) < ] is covariance stationary if:

A process is stationary if:

The first difference of an I(1) time series is weakly dependent.

The model xt = 1xt - 1 + et, t =1,2,…. , where et is an i.i.d. sequence with zero mean and variance 2e represents a(n):

A covariance stationary time series is weakly dependent if:

If adding one more lag of the dependent variable would explain the dependent variable better, then the model is not dynamically complete.

Which of the following is true if yt = + + + + ut is a dynamically complete model?

Under adaptive expectations, the expected current value of a variable does not depend on a recently observed value of the variable.

Weakly dependent processes are said to be integrated of order zero.

Which of the following statements is true?

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

Heteroskedasticity

More on Specification and Data Issues

Basic Regression Analysis 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 Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

The variance of a random walk process decreases as a linear function of time.

Unit root processes, such as a random walk (with or without drift), are said to be:

The model y_t = y_{t -} ₁ + e_t, t = 1, 2, … represents a:

Suppose u_t is the error term for time period 't' in a time series regression model the explanatory variables are x_t = (x_t₁, x_t₂ …., x_tk). The assumption that the errors are contemporaneously homoskedastic implies that:

Which of the following is a strong assumption for static and finite distributed lag models?

A stochastic process {x_t: t = 1,2,….} with a finite second moment [E(x_t²) < ] is covariance stationary if:

The model x_t = ₁x_{t -} ₁+ e_t, t =1,2,…. , where e_t is an i.i.d. sequence with zero mean and variance ²e represents a(n):

Which of the following is true if y_t = + + + + u_t is a dynamically complete model?