A) Var(ut|xt) = 
.
B) Var(ut|xt) = 
.
C) Var(ut|xt) =
2.
D) Var(ut|xt) =
.
Correct Answer
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Correct Answer
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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'.](https://d2lvgg3v3hfg70.cloudfront.net/TB2133/11eab06d_2552_d29d_beed_ad92f679e909_TB2133_11.jpg){kind=small}
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'.](https://d2lvgg3v3hfg70.cloudfront.net/TB2133/11eab06d_2552_d29e_beed_99f48ed6f69c_TB2133_11.jpg){kind=small}
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'.](https://d2lvgg3v3hfg70.cloudfront.net/TB2133/11eab06d_2552_f9af_beed_e930d5ea361a_TB2133_11.jpg){kind=small}
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'.](https://d2lvgg3v3hfg70.cloudfront.net/TB2133/11eab06d_2552_f9b0_beed_e101e419b034_TB2133_11.jpg){kind=small}
1, Cov(xt, xt+h) depends only on 't' and not on 'h'.
Correct Answer
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Correct Answer
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A) unit root processes.
B) trend-stationary processes.
C) serially uncorrelated.
D) asymptotically uncorrelated.
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