Question 1

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 ∞ as h → 0. 
D) the correlation between the independent variable at time 't' and the independent variable at time 't + h' goes to 0 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 ∞ as h → 0. 
D) the correlation between the independent variable at time 't' and the independent variable at time 't + h' goes to 0 as h → ∞. D

Question 2

The model yt = et + β1et - 1 + β2et - 2 ,t = 1,2,….. ,where et is an i.i.d.sequence with zero mean and variance σ2erepresents 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. C

Question 3

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  False

Question 4

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 5

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 6

Sequential exogeneity is implied by dynamic completeness.

Accepted Answer

A) True 
 B)False

Question 7

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 8

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 9

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 10

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 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

The model xt? = α1xt - 1 + et ,t =1,2,…. ,where et is an i.i.d.sequence with zero mean and variance σ2e 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 13

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 14

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

Accepted Answer

A) True 
 B)False

Question 15

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 16

Consider the model: yt = α0 + α1rt1 + α2rt2 + ut.Under weak dependence,the condition sufficient for consistency of OLS is:

Accepted Answer

Question 17

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 18

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 19

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

Accepted Answer

A) True 
 B)False

Question 20

In the model yt = α0 + α1xt1 + α2xt2 + …..+ α?kxtk + ut,the explanatory variables,xt = (xt1,xt2 …. ,xtk),are sequentially exogenous if:

Accepted Answer

A covariance stationary time series is weakly dependent if:

The model y_t = e_t + β₁e_{t -}₁+ β₂e_{t - 2},t = 1,2,….. ,where e_t is an i.i.d.sequence with zero mean and variance σ²_erepresents a(n):

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

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

Which of the following statements is true?

Sequential exogeneity is implied by dynamic completeness.

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:

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:

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

Which of the following statements is true?

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

The model x_t_? = α₁x_{t - 1} + e_t ,t =1,2,…. ,where e_t is an i.i.d.sequence with zero mean and variance σ²_e represents a(n):

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

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

Which of the following is assumed in time series regression?

Consider the model: y_t = α₀ + α₁r_t1 + α₂r_t2 + u_t.Under weak dependence,the condition sufficient for consistency of OLS is:

A process is stationary if:

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

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

In the model y_t = α₀ + α₁x_t1 + α₂x_t2 + …..+ α?_kx_tk + u_t,the explanatory variables,x_t = (x_t1,x_t2 …. ,x_tk),are sequentially exogenous if:

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

A covariance stationary time series is weakly dependent if:

The model yt = et + β1et - 1 + β2et - 2 ,t = 1,2,….. ,where et is an i.i.d.sequence with zero mean and variance σ2erepresents a(n):

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

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

Which of the following statements is true?

Sequential exogeneity is implied by dynamic completeness.

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:

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:

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

Which of the following statements is true?

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

The model xt? = α1xt - 1 + et ,t =1,2,…. ,where et is an i.i.d.sequence with zero mean and variance σ2e represents a(n):

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

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

Which of the following is assumed in time series regression?

Consider the model: yt = α0 + α1rt1 + α2rt2 + ut.Under weak dependence,the condition sufficient for consistency of OLS is:

A process is stationary if:

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

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

In the model yt = α0 + α1xt1 + α2xt2 + …..+ α?kxtk + ut,the explanatory variables,xt = (xt1,xt2 …. ,xtk),are sequentially exogenous if:

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

The model y_t = e_t + β₁e_{t -}₁+ β₂e_{t - 2},t = 1,2,….. ,where e_t is an i.i.d.sequence with zero mean and variance σ²_erepresents 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:

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:

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

The model x_t_? = α₁x_{t - 1} + e_t ,t =1,2,…. ,where e_t is an i.i.d.sequence with zero mean and variance σ²_e represents a(n):

Consider the model: y_t = α₀ + α₁r_t1 + α₂r_t2 + u_t.Under weak dependence,the condition sufficient for consistency of OLS is:

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

In the model y_t = α₀ + α₁x_t1 + α₂x_t2 + …..+ α?_kx_tk + u_t,the explanatory variables,x_t = (x_t1,x_t2 …. ,x_tk),are sequentially exogenous if: