Multiple Choice
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'.](https://d2lvgg3v3hfg70.cloudfront.net/TB2133/11eab06d_2552_ab8c_beed_47c0b7d5d165_TB2133_11.jpg)
] 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'.](https://d2lvgg3v3hfg70.cloudfront.net/TB2133/11eab06d_2552_d29d_beed_ad92f679e909_TB2133_11.jpg)
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)
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)
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)
1, Cov(xt, xt+h) depends only on 't' and not on 'h'.
Correct Answer:
Verified
Correct Answer:
Verified
Q1: Covariance stationarity focuses only on the first
Q2: Which of the following is assumed in
Q10: If a process is said to be
Q18: Under adaptive expectations, the expected current value
Q19: Weakly dependent processes are said to be
Q19: If u<sub>t</sub> refers to the error term
Q20: Which of the following statements is true?<br>A)A
Q23: Covariance stationary sequences where Corr(xt + xt+h)
Q25: If a process is a covariance stationary
Q25: Suppose u<sub>t</sub> is the error term for