Forecasting national activity using lots of international predictors an application to New Zealand
We look at how large international datasets can improve forecasts of national activity. We use the case of New Zealand, an archetypal small open economy. We apply data-rich factor and shrinkage methods to tackle the problem of efficiently handling hundreds of predictor data series from many countrie...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Frankfurt am Main
Deutsche Bundesbank
2009
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| Schriftenreihe: | Discussion paper / Deutsche Bundesbank Series 1, Studies of the Economic Research Centre
No 11/2009 |
| Schlagworte: | |
| Online Zugang: | http://www.bundesbank.de/download/volkswirtschaft/dkp/2009/200911dkp.pdf |
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Tag hinzufügen | Zusammenfassung: | We look at how large international datasets can improve forecasts of national activity. We use the case of New Zealand, an archetypal small open economy. We apply data-rich factor and shrinkage methods to tackle the problem of efficiently handling hundreds of predictor data series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. Using these methods, we assess the marginal predictive content of international data for New Zealand GDP growth. We find that exploiting a large number of international predictors can improve forecasts of our target variable, compared to more traditional models based on small datasets. This is in spite of New Zealand survey data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best. We also assess the type of international data that contains the most predictive information for New Zealand growth over our sample. -- Forecasting ; factor models ; shrinkage methods ; principal components ; targeted predictors ; weighted principal components ; partial least squares ; ridge regression ; elastic net ; international business cycles |
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| Beschreibung: | Literaturverz. S. 24 - 26 |
| Beschreibung: | 51 S. graph. Darst. |
| ISBN: | 9783865585165 978-3-86558-516-5 9783865585172 978-3-86558-517-2 |