Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/177715
Authors: 
Banbura, Marta
van Vlodrop, Andries
Year of Publication: 
2018
Series/Report no.: 
Tinbergen Institute Discussion Paper TI 2018-025/IV
Abstract: 
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.
Subjects: 
Consensus forecasts
forecast evaluation
large cross-sections
state space models
JEL: 
C11
C32
C53
C55
E37
Document Type: 
Working Paper
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