IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v23y2004i6p405-430.html
   My bibliography  Save this article

Combination forecasts of output growth in a seven-country data set

Author

Listed:
  • Mark W. Watson

    (Woodrow Wilson School and Department of Economics, Princeton University and the National Bureau of Economic Research, USA)

  • James H. Stock

    (Department of Economics, Harvard University and the National Bureau of Economic Research, USA)

Abstract

This paper uses forecast combination methods to forecast output growth in a seven-country quarterly economic data set covering 1959-1999, with up to 73 predictors per country. Although the forecasts based on individual predictors are unstable over time and across countries, and on average perform worse than an autoregressive benchmark, the combination forecasts often improve upon autoregressive forecasts. Despite the unstable performance of the constituent forecasts, the most successful combination forecasts, like the mean, are the least sensitive to the recent performance of the individual forecasts. While consistent with other evidence on the success of simple combination forecasts, this finding is difficult to explain using the theory of combination forecasting in a stationary environment. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
  • Handle: RePEc:jof:jforec:v:23:y:2004:i:6:p:405-430
    DOI: 10.1002/for.928
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.928
    File Function: Link to full text; subscription required
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.928?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Figlewski, Stephen & Urich, Thomas, 1983. "Optimal Aggregation of Money Supply Forecasts: Accuracy, Profitability and Market Efficiency," Journal of Finance, American Finance Association, vol. 38(3), pages 695-710, June.
    2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    4. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    6. Figlewski, Stephen, 1983. "Optimal Price Forecasting Using Survey Data," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 13-21, February.
    7. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. Ben S. Bernanke & Ilian Mihov, 1998. "Measuring Monetary Policy," The Quarterly Journal of Economics, Oxford University Press, vol. 113(3), pages 869-902.
    9. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    10. LeSage, James P & Magura, Michael, 1992. "A Mixture-Model Approach to Combining Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 445-452, October.
    11. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    12. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June.
    13. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    14. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    15. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    16. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    17. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    18. Yeung Lewis Chan & James H. Stock & Mark W. Watson, 1999. "A dynamic factor model framework for forecast combination," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 91-121.
    19. Diebold, Francis X. & Pauly, Peter, 1990. "The use of prior information in forecast combination," International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
    20. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    2. Huiyu Huang & Tae-Hwy Lee, 2010. "To Combine Forecasts or to Combine Information?," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 534-570.
    3. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    4. 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.
    5. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    6. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    7. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    8. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    9. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    10. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    11. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    12. Mu-Chun Wang, 2009. "Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 167-182.
    13. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
    14. Lutz Kilian & Atsushi Inoue, 2004. "Bagging Time Series Models," Econometric Society 2004 North American Summer Meetings 110, Econometric Society.
    15. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    16. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    17. Niu, Linlin & Xu, Xiu & Chen, Ying, 2017. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," Economic Modelling, Elsevier, vol. 66(C), pages 201-213.
    18. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    19. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    20. Jean Boivin & Marc P. Giannoni & Ilian Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-384, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:23:y:2004:i:6:p:405-430. 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: . General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.