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Forecasting national activity using lots of international predictors: An application to New Zealand

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  • Eickmeier, Sandra
  • Ng, Tim
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Abstract

We assess the marginal predictive content of a large international dataset for forecasting GDP in New Zealand, an archetypal small open economy. We apply “data-rich” factor and shrinkage methods to efficiently handle hundreds of predictor series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. We find that exploiting a large international dataset can improve forecasts relative to data-rich approaches based on a large national dataset only, and also relative to more traditional approaches based on small datasets. This is in spite of New Zealand’s business and consumer confidence and expectations 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 in handling the international data.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 ()
Pages: 496-511

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Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:496-511

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: 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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Citations

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Cited by:
  1. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
  2. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
  3. Eickmeier, Sandra & Ng, Tim, 2011. "How do credit supply shocks propagate internationally? A GVAR approach," Discussion Paper Series 1: Economic Studies 2011,27, Deutsche Bundesbank, Research Centre.
  4. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo Group Munich.
  5. Halberstadt, Arne & Stapf, Jelena, 2012. "An affine multifactor model with macro factors for the German term structure: Changing results during the recent crises," Discussion Papers 25/2012, Deutsche Bundesbank, Research Centre.
  6. Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank, Research Centre.

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