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  • 1
    Publication Date: 2018-07-03
    Description: In this paper we introduce two general non-parametric first-order stationary time-series models for which marginal (invariant) and transition distributions are expressed as infinite-dimensional mixtures. That feature makes them the first Bayesian stationary fully non-parametric models developed so far. We draw on the discussion of using stationary models in practice, as a motivation, and advocate the view that exible (non-parametric) stationary models might be a source for reliable inferences and predictions. It will be noticed that our models adequately fit in the Bayesian inference framework due to a suitable representation theorem. A stationary scale-mixture model is developed as a particular case along with a computational strategy for posterior inference and predictions. The usefulness of that model is illustrated with the analysis of Euro/USD exchange rate log-returns.
    Keywords: C11 ; C14 ; C15 ; C22 ; C51 ; ddc:330 ; Stationarity ; Markov processes ; Dynamic mixture models ; Random probability measures ; Conditional random probability measures ; Latent processes
    Repository Name: EconStor: OA server of the German National Library of Economics - Leibniz Information Centre for Economics
    Language: Spanish
    Type: doc-type:workingPaper
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