IDEAS home Printed from https://ideas.repec.org/p/igi/igierp/236.html
   My bibliography  Save this paper

Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?

Author

Listed:
  • Anindya Banerjee
  • Massimiliano Marcellino

Abstract

In this paper we evaluate the relative merits of three approaches to information extraction from a large data set for forecasting, namely, the use of an automated model selection procedure, the adoption of a factor model, and single-indicator-based forecast pooling. The comparison is conducted using a large set of indicators for forecasting US inflation and GDP growth. We also compare our large set of leading indicators with purely autoregressive models, using an evaluation procedure that is particularly relevant for policy making. The evaluation is conducted both ex-post and in a pseudo real time context, for several forecast horizons, and using both recursive and rolling estimation. The results indicate a preference for simple forecasting tools, with a good relative performance of pure autoregressive models, and substantial instability in the leading characteristics of the indicators.

Suggested Citation

  • Anindya Banerjee & Massimiliano Marcellino, 2003. "Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?," Working Papers 236, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:236
    as

    Download full text from publisher

    File URL: ftp://ftp.igier.uni-bocconi.it/wp/2003/236.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    2. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    3. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
    4. Filip Keereman, 1999. "The track record of the Commission forecasts," European Economy - Economic Papers 2008 - 2015 137, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    5. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
    6. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    7. Hendry, David F. & Ericsson, Neil R., 1991. "Modeling the demand for narrow money in the United Kingdom and the United States," European Economic Review, Elsevier, vol. 35(4), pages 833-881, May.
    8. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    10. 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.
    11. 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.
    12. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    13. David F. Hendry & Hans-Martin Krolzig, 1999. "Improving on 'Data mining reconsidered' by K.D. Hoover and S.J. Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 202-219.
    14. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    15. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    16. 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.
    17. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
    18. Filip Keereman, 2003. "External assumptions, the international environment and the track record of the Commission Forecast," European Economy - Economic Papers 2008 - 2015 189, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    19. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, February.
    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. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    2. Van Nieuwenhuyze, Christophe & Benk, Szilard & Rünstler, Gerhard & Cristadoro, Riccardo & Den Reijer, Ard & Jakaitiene, Audrone & Jelonek, Piotr & Rua, António & Ruth, Karsten & Barhoumi, Karim, 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
    3. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    4. Serati, Massimiliano & Manera, Matteo & Plotegher, Michele, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," International Energy Markets Working Papers 44426, Fondazione Eni Enrico Mattei (FEEM).
    5. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    7. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Giovanni Veronese, 2001. "A core inflation index for the euro area," Temi di discussione (Economic working papers) 435, Bank of Italy, Economic Research and International Relations Area.
    8. Bruneau, C. & De Bandt, O. & Flageollet, A., 2003. "Forecasting Inflation in the Euro Area," Working papers 102, Banque de France.
    9. O. De Bandt & E. Michaux & C. Bruneau & A. Flageollet, 2007. "Forecasting inflation using economic indicators: the case of France," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 1-22.
    10. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    11. Canova, Fabio, 2002. "G-7 Inflation Forecasts," CEPR Discussion Papers 3283, C.E.P.R. Discussion Papers.
    12. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
    13. Lutz Kilian & Atsushi Inoue, 2004. "Bagging Time Series Models," Econometric Society 2004 North American Summer Meetings 110, Econometric Society.
    14. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    15. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
    16. Curran, Declan & Funke, Michael, 2006. "Taking the temperature : forecasting GDP growth for mainland in China," BOFIT Discussion Papers 6/2006, Bank of Finland, Institute for Economies in Transition.
    17. Troy D. Matheson, 2006. "Factor Model Forecasts for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 2(2), May.
    18. Marie Bessec, 2010. "Etalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Economie & Prévision, La Documentation Française, vol. 0(2), pages 77-99.
    19. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    20. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:igi:igierp:236. 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://www.igier.unibocconi.it/ .

    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: (email available below). General contact details of provider: http://www.igier.unibocconi.it/ .

    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.