Marginal distribution of Markov-switching VAR processes
Gabriele Fiorentini (),
Christophe Planas () and
Alessandro Rossi ()
Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 13, 6605-6623
We make available simple and accurate closed-form approximations to the marginal distribution of Markov-switching vector auto-regressive (MS VAR) processes. The approximation is built upon the property of MS VAR processes of being Gaussian conditionally on any semi-infinite sequence of the latent state. Truncating the semi-infinite sequence and averaging over all possible sequences of that finite length yields a mixture of normals that converges to the unknown marginal distribution as the sequence length increases. Numerical experiments confirm the viability of the approach which extends to the closely related class of MS state space models. Several applications are discussed.
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