Filters
Question type

A stochastic process {xt: t = 1,2,….} with a finite second moment [E(xt2) < A stochastic process {x<sub>t</sub>: t = 1,2,….} with a finite second moment [E(x<sub>t</sub><sup>2</sup>)  <   ] is covariance stationary if: A) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. B) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on h. C)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. D)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on 'h'. ] is covariance stationary if:


A) E(xt) is variable, Var(xt) is variable, and for any t, h A stochastic process {x<sub>t</sub>: t = 1,2,….} with a finite second moment [E(x<sub>t</sub><sup>2</sup>)  <   ] is covariance stationary if: A) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. B) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on h. C)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. D)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on '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 A stochastic process {x<sub>t</sub>: t = 1,2,….} with a finite second moment [E(x<sub>t</sub><sup>2</sup>)  <   ] is covariance stationary if: A) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. B) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on h. C)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. D)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on '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
A stochastic process {x<sub>t</sub>: t = 1,2,….} with a finite second moment [E(x<sub>t</sub><sup>2</sup>)  <   ] is covariance stationary if: A) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. B) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on h. C)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. D)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on '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
A stochastic process {x<sub>t</sub>: t = 1,2,….} with a finite second moment [E(x<sub>t</sub><sup>2</sup>)  <   ] is covariance stationary if: A) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. B) E(x<sub>t</sub>)  is variable, Var(x<sub>t</sub>)  is variable, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on h. C)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 'h' and not on 't'. D)  E(x<sub>t</sub>)  is constant, Var(x<sub>t</sub>)  is constant, and for any t, h   1, Cov(x<sub>t</sub>, x<sub>t+h</sub>)  depends only on 't' and not on 'h'. 1, Cov(xt, xt+h) depends only on 't' and not on 'h'.

Correct Answer

verifed

verified

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

Correct Answer

verifed

verified

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

Correct Answer

verifed

verified

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) Var(ut|xt) = Suppose u<sub>t</sub> is the error term for time period 't' in a time series regression model the explanatory variables are x<sub>t</sub> = (x<sub>t</sub><sub>1</sub>, x<sub>t</sub><sub>2</sub> …., x<sub>tk</sub>) . The assumption that the errors are contemporaneously homoskedastic implies that: A) Var(u<sub>t</sub>|x<sub>t</sub>)  =   . B) Var(u<sub>t</sub>|xt)  =   . C)  Var(u<sub>t</sub>|xt)  =   <sup>2</sup>. D)  Var(u<sub>t</sub>|x<sub>t</sub>)  =   . .
B) Var(ut|xt) = Suppose u<sub>t</sub> is the error term for time period 't' in a time series regression model the explanatory variables are x<sub>t</sub> = (x<sub>t</sub><sub>1</sub>, x<sub>t</sub><sub>2</sub> …., x<sub>tk</sub>) . The assumption that the errors are contemporaneously homoskedastic implies that: A) Var(u<sub>t</sub>|x<sub>t</sub>)  =   . B) Var(u<sub>t</sub>|xt)  =   . C)  Var(u<sub>t</sub>|xt)  =   <sup>2</sup>. D)  Var(u<sub>t</sub>|x<sub>t</sub>)  =   . .
C) Var(ut|xt) =
Suppose u<sub>t</sub> is the error term for time period 't' in a time series regression model the explanatory variables are x<sub>t</sub> = (x<sub>t</sub><sub>1</sub>, x<sub>t</sub><sub>2</sub> …., x<sub>tk</sub>) . The assumption that the errors are contemporaneously homoskedastic implies that: A) Var(u<sub>t</sub>|x<sub>t</sub>)  =   . B) Var(u<sub>t</sub>|xt)  =   . C)  Var(u<sub>t</sub>|xt)  =   <sup>2</sup>. D)  Var(u<sub>t</sub>|x<sub>t</sub>)  =   . 2.
D) Var(ut|xt) =
Suppose u<sub>t</sub> is the error term for time period 't' in a time series regression model the explanatory variables are x<sub>t</sub> = (x<sub>t</sub><sub>1</sub>, x<sub>t</sub><sub>2</sub> …., x<sub>tk</sub>) . The assumption that the errors are contemporaneously homoskedastic implies that: A) Var(u<sub>t</sub>|x<sub>t</sub>)  =   . B) Var(u<sub>t</sub>|xt)  =   . C)  Var(u<sub>t</sub>|xt)  =   <sup>2</sup>. D)  Var(u<sub>t</sub>|x<sub>t</sub>)  =   . .

Correct Answer

verifed

verified

Covariance stationary sequences where Corr(xt + xt+h) Covariance stationary sequences where Corr(xt + xt+h)    0 as   are said to be: A) ​unit root processes. B) trend-stationary processes. C)  ​serially uncorrelated. D)  asymptotically uncorrelated. 0 as Covariance stationary sequences where Corr(xt + xt+h)    0 as   are said to be: A) ​unit root processes. B) trend-stationary processes. C)  ​serially uncorrelated. D)  asymptotically uncorrelated. are said to be:


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

Correct Answer

verifed

verified

Showing 21 - 25 of 25

Related Exams

Show Answer