 Real-time forecast averaging with ALFREDChanont Banternghansa and Michael McCracken Review, 2011, vol. 93, issue Jan, 49-66 Abstract: This paper presents empirical evidence on the efficacy of forecast averaging using the ALFRED (ArchivaL Federal Reserve Economic Data) real-time database. We consider averages over a variety of bivariate vector autoregressive models. These models are distinguished from one another based on at least one of the following factors: (i) the choice of variables used as predictors, (ii) the number of lags, (iii) use of all available data or only data after the Great Moderation, (iv) the observation window used to estimate the model parameters and construct averaging weights, and (v) for the forecast horizons greater than one, the use of either iterated multistep or direct multistep methods. A variety of averaging methods are considered. The results indicate that the benefits of model averaging relative to Bayesian information criterion-based model selection are highly dependent on the class of models averaged The authors provide a novel decomposition of the forecast improvements that allows determination of the most (and least) helpful types of averaging methods and models averaged across. Keywords: Real-time data; Economic forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlrv:y:2011:i:jan:p:49-66:n:v.93no.1 Access Statistics for this article Review is currently edited by Juan M. Sanchez More articles in Review from Federal Reserve Bank of St. Louis Contact information at EDIRC. |
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