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This paper addresses the issue of improving the forecasting performance of Vector AutoRegressions (VARs) when the set of available predictors is inconveniently large to handle with methods and diagnostics in traditional small-scale models. First, available information from a large dataset is summarized into a considerably smaller set of variables through factors estimated using standard principal components. However, even in the case of reducing the dimension of the data the true number of factors may still be large.
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