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Bayesian Inference for Time Series with Stable Innovations
Author(s) -
Qiou Zuqiang,
Ravishanker Nalini
Publication year - 1998
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 - 10.1111/1467-9892.00088
Subject(s) - autoregressive model , bayesian inference , mathematics , inference , series (stratigraphy) , bayesian probability , metropolis–hastings algorithm , fiducial inference , time series , frequentist inference , bayesian linear regression , posterior probability , algorithm , econometrics , computer science , statistics , artificial intelligence , markov chain monte carlo , paleontology , biology
This paper describes Bayesian inference for a linear time series model with stable innovations. An advantage of the Bayesian approach is that it enables the simultaneous estimation of the parameters characterizing the stable law and the parameters of the linear autoregressive moving‐average model. Our approach uses a Metropolis–Hastings algorithm to generate samples from the joint posterior distribution of all the parameters and subsequent inference is based on these samples. We illustrate our approach using data simulated from three linear processes with stable innovations and a real data set