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Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years

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  • Schnatz, Bernd
  • D'Agostino, Antonello

Abstract

Reliable and timely information about current economic conditions is crucial for policy makers and expectations formation. This paper demonstrates the efficacy of the Survey of Professional Forecasters (SPF) and the Purchasing Manager Indices (PMI) in anticipating US real economic activity. We conduct a fully-fledged real-time out-ofsample forecasting exercise linking these surveys to US GDP and industrial production growth over a long sample period. We find that both indicators convey valuable information for assessing current economic conditions. The SPF clearly outperforms the PMI in forecasting GDP growth, while it performs quite poorly in anticipating industrial production growth. Combining the information included in both surveys further improves the accuracy of both, the PMI and the SPF-based forecast. JEL Classification: E37, E47, C22, C53

Suggested Citation

  • Schnatz, Bernd & D'Agostino, Antonello, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20121455
    Note: 231394
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    References listed on IDEAS

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    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. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
    3. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
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    5. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    6. 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.
    7. Liebermann, Joelle, 2010. "Real-time nowcasting of GDP: Factor model versus professional forecasters," MPRA Paper 28819, University Library of Munich, Germany.
    8. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
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    11. Kenny, Geoff & Genre, Véronique & Meyler, Aidan & Timmermann, Allan, 2010. "Combining the forecasts in the ECB survey of professional forecasters: can anything beat the simple average?," Working Paper Series 1277, European Central Bank.
    12. Vermeulen, Philip, 2012. "Quantifying the qualitative responses of the output purchasing managers index in the US and the Euro area," Working Paper Series 1417, European Central Bank.
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    Cited by:

    1. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
    2. Kilinc, Zubeyir & Yucel, Eray, 2016. "PMI Thresholds for GDP Growth," MPRA Paper 70929, University Library of Munich, Germany.
    3. Gabe J. Bondt, 2019. "A PMI-Based Real GDP Tracker for the Euro Area," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(2), pages 147-170, December.
    4. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    business cycle; forecasting; PMI; Real Time Data; US;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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