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Stationary Autoregressive Models via a Bayesian Nonparametric Approach
Author(s) -
Mena Ramsés H.,
Walker Stephen G.
Publication year - 2005
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2005.00429.x
Subject(s) - autoregressive model , mathematics , nonparametric statistics , bayesian probability , type (biology) , density estimation , econometrics , statistics , ecology , estimator , biology
. An approach to constructing strictly stationary AR(1)‐type models with arbitrary stationary distributions and a flexible dependence structure is introduced. Bayesian nonparametric predictive density functions, based on single observations, are used to construct the one‐step ahead predictive density. This is a natural and highly flexible way to model a one‐step predictive/transition density.