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Internal Seminars 2017: Beta-mixing and moments properties of a non-stationary copula-based Markov process

20/06/2017 dalle 13:15 alle 15:15

Dove Dipartimento di Scienze Statistiche, via delle Belle Arti n. 41 -Aula III 2° piano

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Relatore
Fabio Gobbi
Research fellow, Dipartimento di Scienze Statistiche - Bologna

Abstract
We provide conditions under which a non-stationary copula-based Markov process is beta-mixing. We introduce, as a particular case, a convolution-based gaussian Markov process which generalizes the standard random walk allowing the increments to be dependent.
Furthermore, we consider a modified version of a gaussian standard first-order autoregressive process where we allow for a dependence structure between the state variable at the time t-1 and the next innovation. We call this model dependent innovations gaussian AR(1) process (DIG-AR(1)). We analyze the moment and temporal dependence properties of the new model. After proving that the OLS estimator does not consistently estimate the autoregressive parameter, we show how it must be corrected in order to get a consistent estimator which is also asymptotically normal.

Organizzatori
Alessandra Luati e Silvia Cagnone