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Jiahui Wang, E. Zivot (2000)
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The last 10 years data are used in forecasting. RMSFE means the root mean square forecast error. −10 1: Oil and GDP: Cumulative log-predictive likelihoods
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Posterior probability of a break from each univariate model
File Description: Accepted version
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Figure 2: Oil and GDP: Structural change probability
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This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Owing to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to independent breaks in regression coefficients and the volatility parameters. Two empirical applications show the improvements the model has over benchmarks. In a macro application with seven variables we empirically demonstrate the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series.
Journal of Applied Econometrics – Wiley
Published: Jan 1, 2018
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