Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/7059

TitleForecasting in data-rich environments
Author(s)Conraria, Luís Aguiar
Hong, Yongmiao
KeywordsCombination of forecasts
Factor analysis
Forecasting
Inflation
Partial least squares
Principal components
Issue date2004
CitationMIDWEST MACROECONOMICS MEETING, Ames, Estados Unidos da América , 2004 – “Midwest Macroeconomics Meeting : proceedings”. [S.l. : s.n., 2004].
Abstract(s)Stock and Watson (1998 and 1999) developed a factor-model approach which allows for big data sets to be systematically reduced to a few explanatory factors. In this paper two other methods are proposed. The first one, Partial Least Squares is imported from the Chemometrics literature. The second one, which is based on the Combination of Forecasts literature is a modification of Stock and Watson’s method. We will call this method Principal Components Combination. These methods are compared in an empirical application to inflation. We conclude that the method with the best overall performance is the Principal Components Combination.
TypeConference paper
URIhttp://hdl.handle.net/1822/7059
Peer-Reviewedyes
AccessOpen access
Appears in Collections:NIPE - Comunicações a Conferências

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