Bayesian inference and forecasting in the stationary bilinear model

Leon-Gonzalez, Roberto and Yang, Fuyu (2017) Bayesian inference and forecasting in the stationary bilinear model. Communications in Statistics: Theory and Methods, 46 (20). pp. 10327-10347. ISSN 0361-0926

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    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.

    Item Type: Article
    Uncontrolled Keywords: stationary bilinear model,markov chain monte carlo,model comparison
    Faculty \ School: Faculty of Social Sciences > School of Economics
    Depositing User: Pure Connector
    Date Deposited: 24 Sep 2016 01:43
    Last Modified: 06 Nov 2018 15:44
    URI: https://ueaeprints.uea.ac.uk/id/eprint/60196
    DOI: 10.1080/03610926.2016.1235193

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