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Bayesian inference in time series models using kernel quasi likelihoods
Author(s) -
Tsionas Efthymios G.
Publication year - 2002
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.04800
Subject(s) - kernel (algebra) , inference , gibbs sampling , mathematics , series (stratigraphy) , bayesian probability , bayesian inference , covariate , computer science , frequentist inference , fiducial inference , algorithm , bayesian statistics , artificial intelligence , statistics , paleontology , combinatorics , biology
The paper takes up Bayesian inference in time series models when essentially nothing is known about the distribution of the dependent variable given past realizations or other covariates. It proposes the use of kernel quasi likelihoods upon which formal inference can be based. Gibbs sampling with data augmentation is used to perform the computations related to numerical Bayesian analysis of the model. The method is illustrated with artificial and real data sets.