Question 1

Covariance stationary sequences where Corr(xt + xt+h)   0 as   are said to be:

Accepted Answer

A) ​unit root processes. 
B) trend-stationary processes. 
C) ​serially uncorrelated. 
D) asymptotically uncorrelated. 
A) ​unit root processes. 
B) trend-stationary processes. 
C) ​serially uncorrelated. 
D) asymptotically uncorrelated.

Question 2

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 3

Sequential exogeneity is implied by dynamic completeness.

Accepted Answer

A) True 
 B)False

Question 4

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

Accepted Answer

Question 5

If a process is a covariance stationary process, then it will have a finite second moment.

Accepted Answer

A) True 
 B)False

Question 6

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 7

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 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, ….

Covariance stationary sequences where Corr(xt + xt+h) 0 as are said to be:

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:

Sequential exogeneity is implied by dynamic completeness.

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

If a process is a covariance stationary process, then it will have a finite second moment.

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?

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:

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 stationary sequences where Corr(xt + xt+h) 0 as are said to be:

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:

Sequential exogeneity is implied by dynamic completeness.

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

If a process is a covariance stationary process, then it will have a finite second moment.

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?

In the model yt = 0 + 1xt1 + 2xt2 + ….. + 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

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

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:

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

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):

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: