In this paper, I propose the use of fast Fourier transform (FFT) as a convenient tool for combining forecast densities of vector autoregressive models in a hybrid Bayesian manner. While a vast amount of papers comprises combinations based on normal approximations, Monte Carlo methods were fully utilized here, which made the analysis computationally demanding. For the sake of minimization of computational time, the FFT algorithm was used to combine the densities of poorly simulated partial models. As a result, a minor loss of quality in the final combined model was allowed, in contrast with the reduction in the necessary simulation time. However, it turns out in the end that the FFT-based approach exceeds ´brute-force´ simulation in all aspects. The suggested method is demonstrated on an ex ante prediction of the Czech GDP and on a pair of artificial examples. If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large. As the access to this document is restricted, you may want to look for a different version under ‘Related research’ (further below) or search for a different version of it. Keywords: Bayesian model averaging, fast Fourier transform, Markov chain Monte Carlo, vector autoregressions, References listed on IDEAS Please report citation or reference errors to Jose.Barrueco@uv.es, or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on ‘citations’ and make appropriate adjustments.: When requesting a correction, please mention this item’s handle: RePEc:prg:jnlaop:v:2010:y:2010:i:5:id:318:p:72-88. See general information about how to correct material in RePEc. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: firstname.lastname@example.org (Frantisek Sokolovsky) If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about. If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form. If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the ‘citations’ tab in your profile, as there may be some citations waiting for confirmation. Please note that corrections may take a couple of weeks to filter through the various RePEc services. Source.