Combining Multivariate Density Forecasts Using Predictive Criteria

25 Pages Posted: 7 Apr 2009

See all articles by Kristoffer P. Nimark

Kristoffer P. Nimark

Universitat Pompeu Fabra

Hugo Gerard

affiliation not provided to SSRN

Date Written: April 6, 2009

Abstract

This paper combines multivariate density forecasts of output growth, inflation and interest rates from a suite of models. An out-of-sample weighting scheme based on the predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson and Karlsson (2007) is used to combine the models. Three classes of models are considered: a Bayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR) and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australian data, we find that, at short forecast horizons, the Bayesian VAR model is assigned the most weight, while at intermediate and longer horizons the factor model is preferred. The DSGE model is assigned little weight at all horizons, a result that can be attributed to the DSGE model producing density forecasts that are very wide when compared with the actual distribution of observations. While a density forecast evaluation exercise reveals little formal evidence that the optimally combined densities are superior to those from the best-performing individual model, or a simple equal-weighting scheme, this may be a result of the short sample available.

Keywords: Density forecasts, combining forecasts, predictive criteria

Suggested Citation

Nimark, Kristoffer P. and Gerard, Hugo, Combining Multivariate Density Forecasts Using Predictive Criteria (April 6, 2009). Available at SSRN: https://ssrn.com/abstract=1373713 or http://dx.doi.org/10.2139/ssrn.1373713

Kristoffer P. Nimark (Contact Author)

Universitat Pompeu Fabra ( email )

Ramon Trias Fargas, 25-27
Barcelona, E-08005
Spain

Hugo Gerard

affiliation not provided to SSRN ( email )

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