Question 1

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

Accepted Answer

A) True 
 B)False

Question 2

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 3

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 4

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

Accepted Answer

A) True 
 B)False

Question 5

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

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

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

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

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

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 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

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

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

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

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

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 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

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

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: