Bayesian Inference and Forecasting in the Stationary Bilinear Model
AbstractA stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. The SB model can be used to model highly persistent macroeconomic time series such as inflation. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using U.K. inflation data, we find that the SB model outperforms the random walk and first order autoregressive AR(1) models, in terms of root mean squared forecast errors for the one-step-ahead out-of-sample forecast. In addition, the SB model is superior to these two models in terms of predictive likelihood for almost all of the forecast observations.
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Bibliographic InfoPaper provided by School of Economics, University of East Anglia, Norwich, UK. in its series University of East Anglia Applied and Financial Economics Working Paper Series with number 055.
Date of creation: Jan 2014
Date of revision:
Postal: Helen Chapman, School of Economics, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-01-10 (All new papers)
- NEP-ECM-2014-01-10 (Econometrics)
- NEP-ETS-2014-01-10 (Econometric Time Series)
- NEP-FOR-2014-01-10 (Forecasting)
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