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Nowcasting Using the Chicago Fed National Activity Index

Author

Listed:
  • Scott Brave
  • R. Andrew Butters

Abstract

The authors present an alternative version of the Chicago Fed National Activity Index (CFNAI), which is constructed using a methodology that allows for a more robust treatment of the underlying data series than its traditional methodology. This alternative CFNAI produces superior predictions of real gross domestic product growth for the current quarter (nowcasts) while correlating more closely with U.S. recessions than the traditional index.

Suggested Citation

  • Scott Brave & R. Andrew Butters, 2014. "Nowcasting Using the Chicago Fed National Activity Index," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 19-37.
    • Handle: RePEc:fip:fedhep:00005
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    References listed on IDEAS

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    1. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    2. 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.
    3. Scott Brave, 2008. "Economic trends and the Chicago Fed National Activity Index," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue May.
    4. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. Baker, Stuart G. & Kramer, Barnett S., 2007. "Peirce, Youden, and Receiver Operating Characteristic Curves," The American Statistician, American Statistical Association, vol. 61, pages 343-346, November.
    7. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    8. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    9. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    10. Jungbacker, B. & Koopman, S.J. & van der Wel, M., 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1358-1368, August.
    11. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    12. Jonas D. M. Fisher, 2000. "Forecasting inflation with a lot of data," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue Mar.
    13. Scott Brave & R. Andrew Butters, 2013. "Estimating the trend rate of economic growth using the CFNAI," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue June.
    14. Charles L. Evans & Chin Te Liu & Genevieve Pham-Kanter, 2002. "The 2001 recession and the Chicago Fed National Index: identifying business cycle turning points," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 26(Q III), pages 26-43.
    15. 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.
    16. Oscar Jorda & Travis Berge, 2009. "The Classification of Economic Activity into Expansions and Recessions," Working Papers 918, University of California, Davis, Department of Economics.
    17. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
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    Cited by:

    1. Abdalla, Ahmed & Carabias, Jose M. & Patatoukas, Panos N., 2021. "The real-time macro content of corporate financial reports: a dynamic factor model approach," LSE Research Online Documents on Economics 108539, London School of Economics and Political Science, LSE Library.
    2. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    3. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    4. Carabias, Jose M., 2018. "The real-time information content of macroeconomic news: implications for firm-level earnings expectations," LSE Research Online Documents on Economics 86399, London School of Economics and Political Science, LSE Library.
    5. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    6. Abdalla, Ahmed M. & Carabias, Jose M. & Patatoukas, Panos N., 2021. "The real-time macro content of corporate financial reports: A dynamic factor model approach," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 260-280.
    7. Sean P. Grover & Kevin L. Kliesen & Michael W. McCracken, 2016. "A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth," Review, Federal Reserve Bank of St. Louis, vol. 98(4), pages 277-296.
    8. Jose M. Carabias, 2018. "The real-time information content of macroeconomic news: implications for firm-level earnings expectations," Review of Accounting Studies, Springer, vol. 23(1), pages 136-166, March.
    9. Jiménez Polanco, Miguel Alejandro & López Hawa, Nabil & Ramírez Escoboza, Merlym, 2016. "Indicadores Compuestos de Actividad Económica por sectores para la República Dominicana [Composite Indicators of Economic Activity for the Dominican Republic]," MPRA Paper 75916, University Library of Munich, Germany.

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