Bayesian inference and forecasting in the stationary bilinear model

Roberto Leon-Gonzalez, Fuyu Yang

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A stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using monthly U.K. inflation data, we find that the SB model outperforms the random walk, first order autoregressive AR(1), and autoregressive moving average ARMA(1,1) models in terms of root mean squared forecast errors. In addition, the SB model is superior to these three models in terms of predictive likelihood for the majority of forecast observations.
Original languageEnglish
Pages (from-to)10327-10347
Number of pages21
JournalCommunications in Statistics: Theory and Methods
Issue number20
Early online date12 Oct 2016
Publication statusPublished - 2017


  • Stationary bilinear model
  • Markov chain Monte Carlo
  • model comparison

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