IDEAS home Printed from https://ideas.repec.org/p/ptu/wpaper/w201502.html
   My bibliography  Save this paper

Macroeconomic Forecasting Starting from Survey Nowcasts

Author

Listed:
  • João Valle e Azevedo
  • Inês Maria Gonçalves

Abstract

We explore the use of nowcasts from the Philadelphia Survey of Professional Forecasters as a starting point for macroeconomic forecasting. Specifically, survey nowcasts are treated as an additional observation of the time series of interest. This simple approach delivers enhanced model performance through the straightforward use of timely information. Important gains in forecast accuracy are observed for multiple methods/models, especially at shorter horizons. Still, given that survey nowcasts are very hard to beat, this approach proves most useful as a means of developing a sharper forecasting routine for longer-term predictions.

Suggested Citation

  • João Valle e Azevedo & Inês Maria Gonçalves, 2015. "Macroeconomic Forecasting Starting from Survey Nowcasts," Working Papers w201502, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w201502
    as

    Download full text from publisher

    File URL: https://www.bportugal.pt/sites/default/files/anexos/papers/wp201502.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    3. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2008. "Explaining The Great Moderation: It Is Not The Shocks," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 621-633, 04-05.
    4. Maik H. Wolters, 2015. "Evaluating Point and Density Forecasts of DSGE Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 74-96, January.
    5. Wolters, Maik H., 2011. "Forecasting under Model Uncertainty," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48723, Verein für Socialpolitik / German Economic Association.
    6. Marcin Kolasa & Michał Rubaszek & Paweł Skrzypczyński, 2012. "Putting the New Keynesian DSGE Model to the Real‐Time Forecasting Test," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1301-1324, October.
    7. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    8. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    9. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    10. Rubaszek, Michal & Skrzypczynski, Pawel, 2008. "On the forecasting performance of a small-scale DSGE model," International Journal of Forecasting, Elsevier, vol. 24(3), pages 498-512.
    11. Ana Pereira & João Valle e Azevedo, 2013. "Macroeconomic Forecasting Using Low-Frequency Filters," Working Papers w201301, Banco de Portugal, Economics and Research Department.
    12. Joelle Liebermann, 2014. "Real-Time Nowcasting of GDP: A Factor Model vs. Professional Forecasters," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(6), pages 783-811, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:ptu:wpaper:w201502. 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: (DEE-NTD). General contact details of provider: http://edirc.repec.org/data/bdpgvpt.html .

    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.