 A medium-N approach to macroeconomic forecastingGianluca Cubadda and Barbara Guardabascio Economic Modelling, 2012, vol. 29, issue 4, 1099-1105 Abstract: This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting. Keywords: Partial least squares; Principal component regression; Dynamic factor models; Data-rich forecasting methods; Dimension-reduction techniques (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:eee:ecmode:v:29:y:2012:i:4:p:1099-1105 DOI: 10.1016/j.econmod.2012.03.027 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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