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/TB8272/11eb6b8a_02a7_9d09_bf83_7b7fec60869d_TB8272_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/TB8272/11eb6b8a_02a7_c41a_bf83_636ba60c025e_TB8272_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/TB8272/11eb6b8a_02a7_c41b_bf83_b76ad0b9a8ee_TB8272_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/TB8272/11eb6b8a_02a7_eb2c_bf83_3b54b8e19a47_TB8272_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/TB8272/11eb6b8a_02a7_eb2d_bf83_1b1dcecac957_TB8272_11.jpg)
1, Cov(xt, xt+h) depends only on 't' and not on 'h'.
Correct Answer:
Verified
Correct Answer:
Verified
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