Question 1

Consider the model: yt =   0 +   1zt1 +   2zt2 + ut. Under weak dependence, the condition sufficient for consistency of OLS is:

Accepted Answer

Question 2

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

Accepted Answer

A) True 
 B)False  False

Question 3

Sequential exogeneity is implied by dynamic completeness.

Accepted Answer

A) True 
 B)False  True

Question 4

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 5

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 6

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 7

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 8

In the model yt =   0 +   1xt1 +   2xt2 + ….. +   kxtk + ut, the explanatory variables, xt = (xt1, xt2 …., xtk), are sequentially exogenous if:

Accepted Answer

A) E(ut|xt , xt-1, ……) = E(ut) = 0, t = 1,2, …. 
B) E(ut|xt , xt-1, ……)   E(ut) = 0, t = 1,2, …. 
C)  E(ut|xt , xt-1, ……) = E(ut) > 0, t = 1,2, …. 
D)  E(ut|xt , xt-1, ……) = E(ut) = 1, t = 1,2, …. 
A) E(ut|xt , xt-1, ……) = E(ut) = 0, t = 1,2, …. 
B) E(ut|xt , xt-1, ……)   E(ut) = 0, t = 1,2, …. 
C)  E(ut|xt , xt-1, ……) = E(ut) > 0, t = 1,2, …. 
D)  E(ut|xt , xt-1, ……) = E(ut) = 1, t = 1,2, ….

Question 9

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 10

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

Accepted Answer

A) static model. 
B) moving average process of order one. 
C)  moving average process of order two. 
D)  autoregressive process of order two. 
A) static model. 
B) moving average process of order one. 
C)  moving average process of order two. 
D)  autoregressive process of order two.

Question 11

Which of the following statements is true of dynamically complete models?

Accepted Answer

A) There is scope of adding more lags to the model to better forecast the dependent variable. 
B) The problem of serial correlation does not exist in dynamically complete models. 
C)  All econometric models are dynamically complete. 
D)  Sequential endogeneity is implied by dynamic completeness.. 
A) There is scope of adding more lags to the model to better forecast the dependent variable. 
B) The problem of serial correlation does not exist in dynamically complete models. 
C)  All econometric models are dynamically complete. 
D)  Sequential endogeneity is implied by dynamic completeness..

Question 12

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 13

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 14

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 15

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 16

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.

Question 17

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.

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

If ut refers to the error term at time 't' and yt - 1 refers to the dependent variable at time 't - 1', for an AR(1) process to be homoskedastic, it is required that:

Accepted Answer

Question 20

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

Accepted Answer

A) True 
 B)False

Consider the model: y_t = ₀ + ₁z_t₁ + ₂z_t₂ + u_t. Under weak dependence, the condition sufficient for consistency of OLS is:

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

Sequential exogeneity is implied by dynamic completeness.

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 is a strong assumption for static and finite distributed lag models?

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

A process is stationary if:

In the model y_t = ₀ + ₁x_t₁ + ₂x_t₂ + ….. + _kx_tk + u_t, the explanatory variables, x_t = (x_t₁, x_t₂ …., x_tk), are sequentially exogenous if:

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

The model y_t = e_t + ₁e_{t -} ₁ + ₂e_{t -} ₂ , 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 statements is true of dynamically complete models?

A covariance stationary time series is weakly dependent 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 statements is true?

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

Which of the following is assumed in time series regression?

Which of the following statements is true?

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

If u_t refers to the error term at time 't' and y_{t -} ₁ refers to the dependent variable at time 't - 1', for an AR(1) process to be homoskedastic, it is required that:

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

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

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 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 11: Further Issues in Using Ols With Time Series Data

Consider the model: yt = 0 + 1zt1 + 2zt2 + ut. Under weak dependence, the condition sufficient for consistency of OLS is:

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

Sequential exogeneity is implied by dynamic completeness.

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 is a strong assumption for static and finite distributed lag models?​

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

A process is stationary if:

In the model yt = 0 + 1xt1 + 2xt2 + ….. + kxtk + ut, the explanatory variables, xt = (xt1, xt2 …., xtk), are sequentially exogenous if:

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

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

Which of the following statements is true of dynamically complete models?

A covariance stationary time series is weakly dependent if:

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):

Which of the following statements is true?

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

Which of the following is assumed in time series regression?

Which of the following statements is true?

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

If ut refers to the error term at time 't' and yt - 1 refers to the dependent variable at time 't - 1', for an AR(1) process to be homoskedastic, it is required that:

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

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

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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Consider the model: y_t = ₀ + ₁z_t₁ + ₂z_t₂ + u_t. Under weak dependence, the condition sufficient for consistency of OLS is:

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

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

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

In the model y_t = ₀ + ₁x_t₁ + ₂x_t₂ + ….. + _kx_tk + u_t, the explanatory variables, x_t = (x_t₁, x_t₂ …., x_tk), are sequentially exogenous if:

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

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

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):

If u_t refers to the error term at time 't' and y_{t -} ₁ refers to the dependent variable at time 't - 1', for an AR(1) process to be homoskedastic, it is required that: