Premium
Bayesian Subset Model Selection for Time Series
Author(s) -
Unnikrishnan N. K.
Publication year - 2004
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.1467-9892.2004.01874.x
Subject(s) - setar , autoregressive model , mathematics , series (stratigraphy) , model selection , nonlinear autoregressive exogenous model , bayesian probability , selection (genetic algorithm) , markov chain , star model , time series , econometrics , bilinear interpolation , markov chain monte carlo , statistics , autoregressive integrated moving average , computer science , artificial intelligence , paleontology , biology
. This paper considers the problem of subset model selection for time series. In general, a few lags which are not necessarily continuous, explain lag structure of a time‐series model. Using the reversible jump Markov chain technique, the paper develops a fully Bayesian solution for the problem. The method is illustrated using the self‐exciting threshold autoregressive (SETAR), bilinear and AR models. The Canadian lynx data, the Wolfe's sunspot numbers and Series A of Box and Jenkins (1976) are analysed in detail.