To qualify as a covariance stationary process, which of the following does not have to be true? A) E[xt] = E[xt+1]. B) Covariance(xt, xt-2) = Covariance(xt, xt+2). C) Covariance(xt, xt-1) = Covariance(xt, xt-2). t, t-1, t-2, t+2 are subscripts.
C?
A) E[xt] = E[xt+1]. ?
I know the 3 conditions are: 1. constant & finite expected value 2. constant & finite expected variance 3. constant & finite expected covariance b/w leading & lagged values I think 1 corresponds to A, & 3 corresponds to B. I believe C does not satisfy one of the 3 conditions but please confirm.
cpk123 Wrote: ------------------------------------------------------- > A) E = E. ? that’s what i thought Your answer: A was incorrect. The correct answer was C) Covariance(xt, xt-1) = Covariance(xt, xt-2). If a series is covariance stationary then the unconditional mean is constant across periods. The unconditional mean or expected value is the same from period to period: E[xt] = E[xt+1]. The covariance between any two observations equal distance apart will be equal, e.g., the t and t-2 observations with the t and t+2 observations. The one relationship that does not have to be true is the covariance between the t and t-1 observations equaling that of the t and t-2 observations. so i guess they would have to be the same num. of spaces apart - e.g., covariance (xt, xt-1) = covariance (xt-1, xt-2) would be a requirement for covariance stationarity
so basically topher was 100% correct
yes