Question

It should be clear from the discussion that a strictly stationary, finite variance, time series is also stationary. However, the converse may or may not be true, in general. A time series {Xti t 0,?1,?2, ) is said to be a Gaussian process, if the n-dimensional random vectors X = (Xti, Xt2, .. . , X.). for every collection of time points t1, t2,..., tn, and every positive integer n, have a Multivariate Normal distribution. } is a stationary Gaussian Show that if the time series {Xt; t = 0,?1,42, process, then it is strictly stationary, as well.

Answer 1

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