Combining multivariate density forecasts using predictive criteria

Hugo Gerard and Kristoffer Nimark ()

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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 (search for similar items in EconPapers)
Date: 2008-08, Revised 2008-10
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (28)

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Working Paper: Combining Multivariate Density Forecasts Using Predictive Criteria (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:1117

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