Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean
Marta Banbura () and
Andries van Vlodrop ()
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Andries van Vlodrop: VU Amsterdam, the Netherlands
No 18-025/IV, Tinbergen Institute Discussion Papers from Tinbergen Institute
We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are modelled via stochastic volatility. The proposed Gibbs sampler allows the researcher to use a large cross-sectional dimension in a feasible amount of computational time. The slowly changing mean can account for a number of secular developments such as changing inflation expectations, slowing productivity growth or demographics. We show the good forecasting performance of the model relative to popular alternatives, including standard Bayesian VARs with Minnesota priors, VARs with democratic priors and standard time-varying parameter VARs for the euro area, the United States and Japan. In particular, incorporating survey forecast information helps to reduce the uncertainty about the unconditional mean and along with the time variation improves the long-run forecasting performance of the VAR models.
Keywords: Consensus forecasts; forecast evaluation; large cross-sections; state space models. (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 C55 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20180025
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