Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean

Tinbergen Institute Discussion Paper 2018-025/IV

54 Pages Posted: 26 Mar 2018

See all articles by Marta Banbura

Marta Banbura

European Central Bank

Andries van Vlodrop

VU University Amsterdam

Date Written: March 8, 2018


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

JEL Classification: C11, C32, C53, C55, E37

Suggested Citation

Banbura, Marta and van Vlodrop, Andries, Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean (March 8, 2018). Tinbergen Institute Discussion Paper 2018-025/IV. Available at SSRN: or

Marta Banbura (Contact Author)

European Central Bank ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314

Andries Van Vlodrop

VU University Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics