Bootstrap multi-step forecasts of non-Gaussian VAR models

In this paper, we establish the asymptotic validity and analyse the finite sample performance of a simple bootstrap procedure for constructing multi-step multivariate forecast densities in the context of non-Gaussian unrestricted VAR models. This bootstrap procedure avoids the backward representation, and, as a consequence, can be used to obtain multivariate forecast densities in, for example, VARMA or VAR-GARCH models. In the context of bivariate stationary VAR(p) models, we show that its finite sample properties are comparable to those of alternatives based on the backward representation. The bootstrap procedure is also implemented in a VAR-DCC model which lacks a backward representation. Finally, joint forecast densities of US quarterly inflation, unemployment and GDP growth are obtained. (C) 2014 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

bias correction; dcc model; forecast density; forecast regions; high density regions; lag order uncertainty; multivariate forecast; resampling methods

Bootstrap multi-step forecasts of non-Gaussian VAR models Articles