IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

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

  • Eickmeier, Sandra
  • Ng, Tim

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/pii/S0169207010000038
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

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

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

as
in new window

Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:496-511
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. M. Hashem Pesaran & Til Schuermann & Scott M. Weiner, 2002. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Center for Financial Institutions Working Papers 01-38, Wharton School Center for Financial Institutions, University of Pennsylvania.
  2. Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
  3. Stephane Dees & Filippo di Mauro & M. Hashem Pesaran & L. Vanessa Smith, 2005. "Exploring the International Linkages of the Euro Area: a Global VAR Analysis," CESifo Working Paper Series 1425, CESifo Group Munich.
  4. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?," Working Paper Series 0700, European Central Bank.
  5. 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.
  6. Alfred A. Haug & Christie Smith, 2012. "Local Linear Impulse Responses for a Small Open Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(3), pages 470-492, 06.
  7. Matheson, Troy D, 2006. "Factor Model Forecasts for New Zealand," MPRA Paper 807, University Library of Munich, Germany.
  8. 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.
  9. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  10. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
  11. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
  12. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
  13. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320.
  14. Dées, Stéphane & Vansteenkiste, Isabel, 2007. "The transmission of US cyclical developments to the rest of the world," Working Paper Series 0798, European Central Bank.
  15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  16. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  17. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  18. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Working Papers 07-8, Bank of Canada.
  19. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
  20. Eickmeier, Sandra & Lemke, Wolfgang & Marcellino, Massimiliano, 2011. "Classical time-varying FAVAR models - estimation, forecasting and structural analysis," Discussion Paper Series 1: Economic Studies 2011,04, Deutsche Bundesbank, Research Centre.
  21. Sandra Eickmeier, 2009. "Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 933-959.
  22. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 0482, European Central Bank.
  23. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
  24. John C. Robertson, 2000. "Central bank forecasting: an international comparison," Economic Review, Federal Reserve Bank of Atlanta, issue Q2, pages 21-32.
  25. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  26. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Working Papers 01-18, Bank of Canada.
  27. Buckle, Robert A. & Kim, Kunhong & Kirkham, Heather & McLellan, Nathan & Sharma, Jarad, 2007. "A structural VAR business cycle model for a volatile small open economy," Economic Modelling, Elsevier, vol. 24(6), pages 990-1017, November.
  28. Dées, Stéphane & Saint-Guilhem, Arthur, 2009. "The role of the United States in the global economy and its evolution over time," Working Paper Series 1034, European Central Bank.
  29. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  30. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  31. Marc Brisson & Bryan Campbell & John Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.
  32. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  33. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  34. Chen, Yu-chin & Rogoff, Kenneth, 2003. "Commodity currencies," Journal of International Economics, Elsevier, vol. 60(1), pages 133-160, May.
  35. Mike Frith & Aaron Drew, 1998. "Forecasting at the Reserve Bank of New Zealand," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 61, December.
  36. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:496-511. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.