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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 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. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2009,11.

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Date of creation: 2009
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Handle: RePEc:zbw:bubdp1:7580

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Keywords: Forecasting; factor models; shrinkage methods; principal components; targeted predictors; weighted principal components; partial least squares;

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Citations

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Cited by:
  1. 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.
  2. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
  3. Eickmeier, Sandra & Ng, Tim, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Discussion Paper Series 1: Economic Studies 2009,11, Deutsche Bundesbank, Research Centre.
  4. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
  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. 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.
  8. 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.
  9. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo Group Munich.

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