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Bayesian inference and forecasting in the stationary bilinear model

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  • Roberto Leon-Gonzalez
  • Fuyu Yang

Abstract

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.

Suggested Citation

  • Roberto Leon-Gonzalez & Fuyu Yang, 2017. "Bayesian inference and forecasting in the stationary bilinear model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(20), pages 10327-10347, October.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:20:p:10327-10347
    DOI: 10.1080/03610926.2016.1235193
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