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Bayesian Inference and Forecasting in the Stationary Bilinear Model

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  • Roberto Leon-Gonzalez

    (National Graduate Institute for Policy Studies)

  • Fuyu Yang

    (University of East Anglia)

Abstract

A 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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Paper 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.

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Date of creation: Jan 2014
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Handle: RePEc:uea:aepppr:2012_55

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  1. Wojciech Charemza & Yuriy Kharin & Vladislav Maevskiy, 2012. "Bilinear forecast risk assessment for non-systematic inflation: Theory and evidence," Discussion Papers in Economics 12/22, Department of Economics, University of Leicester.
  2. Francq, Christian & Makarova, Svetlana & Zakoi[diaeresis]an, Jean-Michel, 2008. "A class of stochastic unit-root bilinear processes: Mixing properties and unit-root test," Journal of Econometrics, Elsevier, vol. 142(1), pages 312-326, January.
  3. Brunner, Allan D. & Hess, Gregory D., 1995. "Potential problems in estimating bilinear time-series models," Journal of Economic Dynamics and Control, Elsevier, vol. 19(4), pages 663-681, May.
  4. Charemza W.W. & M. Lifshits & S. Makarova, 2002. "Conditional testing for unit-root bilinearity in financial time series: some theoretical and empirical results," Computing in Economics and Finance 2002 251, Society for Computational Economics.
  5. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
  6. J. D. Byers & D. A. Peel, 1995. "Bilinear quadratic ARCH and volatility spillovers in inter-war exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 2(7), pages 215-219.
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