Bayesian Subset Model Selection for Time Series

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. Copyright 2004 Blackwell Publishing Ltd.