Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/83714
Authors: 
Martínez-Ovando, Juan Carlos
Walker, Stephen G.
Year of Publication: 
2011
Series/Report no.: 
Working Papers, Banco de México 2011-08
Abstract (Translated): 
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.
Subjects: 
Stationarity
Markov processes
Dynamic mixture models
Random probability measures
Conditional random probability measures
Latent processes
JEL: 
C11
C14
C15
C22
C51
Document Type: 
Working Paper
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