IDEAS home Printed from
   My bibliography  Save this article

Using monthly data to predict quarterly output


  • Robert Ingenito
  • Bharat Trehan


Some time ago, the Commerce Department changed the way it calculates real gross domestic product. In response to that change, this paper presents an update of a simple model that is used to predict the growth rate of current quarter real output based on available monthly data. After searching over a set containing more than 30 different variables, we find that a model that utilized monthly data on consumption and nonfarm payroll employment to predict contemporaneous real GDP does best.

Suggested Citation

  • Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
  • Handle: RePEc:fip:fedfer:y:1996:p:3-11:n:3

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
    2. Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, vol. 88(351), pages 549-563, September.
    3. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    4. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
    5. 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.
    6. Brian Motley, 1992. "Index numbers and the measurement of real GDP," Economic Review, Federal Reserve Bank of San Francisco, pages 3-13.
    7. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    8. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
    9. Bharat Trehan, 1992. "Predicting contemporaneous output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    2. Julio Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters,in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361 National Bureau of Economic Research, Inc.
    3. Bennett T. McCallum & Edward Nelson, 1999. "Performance of Operational Policy Rules in an Estimated Semiclassical Structural Model," NBER Chapters,in: Monetary Policy Rules, pages 15-56 National Bureau of Economic Research, Inc.
    4. Doll, Jens & Rosenthal, Beatrice & Volkenand, Jonas & Hamella, Sandra, 2017. "Nowcasting des deutschen BIP," Weidener Diskussionspapiere 59, University of Applied Sciences Amberg-Weiden (OTH).
    5. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    6. Dennis L. Hoffman & Robert H. Rasche, 1997. "STLS/US-VECM6.1: a vector error-correction forecasting model of the U. S. economy," Working Papers 1997-008, Federal Reserve Bank of St. Louis.
    7. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP
      [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]
      ," MPRA Paper 63713, University Library of Munich, Germany.
    8. Castilla, Adolfo, 2015. "Proyecto LINK y Econometría de Alta Frecuencia: Las últimas aportaciones econométricas de Lawrence R. Klein /LINK Project and High Frequency Econometrics: Recent Econometric Contributions of Lawrence ," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 33, pages 421-450, Mayo.
    9. Konstantins Benkovskis, 2008. "Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators," Working Papers 2008/05, Latvijas Banka.
    10. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    11. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
    12. Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
    13. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
    14. Kitchen, John & Monaco, Ralph, 2003. "Real-Time Forecasting in Practice: The U.S. Treasury Staff's Real-Time GDP Forecast System," MPRA Paper 21068, University Library of Munich, Germany, revised Oct 2003.
    15. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    16. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    17. Fabio Ghironi, 2000. "Alternative Monetary Rules for a Small Open Economy: The Case of Canada," Boston College Working Papers in Economics 466, Boston College Department of Economics, revised 30 Oct 2000.
    18. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.
    19. Elisa Keller, 2007. "Classical and Bayesian Methods for the VAR Analysis: International Comparisons," Rivista di Politica Economica, SIPI Spa, vol. 97(6), pages 149-202, November-.
    20. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 276, European Central Bank.
    21. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    22. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.


    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:fip:fedfer:y:1996:p:3-11:n:3. 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: (Federal Reserve Bank of San Francisco Research Library). General contact details of provider: .

    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 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.

    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.