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
Q1: Which of the following is a strong
Q2: If a process is said to be
Q4: A process is stationary if:
A)any collection of
Q5: Consider the model: yt = 
Q6: Which of the following statements is true
Q7: Covariance stationarity focuses only on the first
Q8: In the model yt =
Q9: The model yt = yt - 1
Q10: Which of the following statements is true?
A)A
Q11: Suppose ut is the error term for
Unlock this Answer For Free Now!
View this answer and more for free by performing one of the following actions
Scan the QR code to install the App and get 2 free unlocks
Unlock quizzes for free by uploading documents