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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

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. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2012. "Sparse partial least squares in time series for macroeconomic forecasting," Statistics and Econometrics Working Papers ws122216, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Gianluca Cubadda & Barbara Guardabascio, 2010. "A Medium-N Approach to Macroeconomic Forecasting," CEIS Research Paper 176, Tor Vergata University, CEIS, revised 09 Dec 2010.
  3. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
  4. Lehmann, Robert & Wohlrabe, Klaus, 2013. "Forecasting GDP at the regional level with many predictors," Discussion Papers in Economics 17104, University of Munich, Department of Economics.
  5. Schumacher, Christian, 2010. "Factor forecasting using international targeted predictors: The case of German GDP," Economics Letters, Elsevier, vol. 107(2), pages 95-98, May.
  6. 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.
  7. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
  8. 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.
  9. 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.

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