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Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach

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  • MICHELLE T. ARMESTO
  • RUB…N HERN¡NDEZ-MURILLO
  • MICHAEL T. OWYANG
  • JEREMY PIGER

Abstract

Studies of the predictive ability of the Federal Reserve's Beige Book for aggregate output and employment have proven inconclusive. This might be attributed, in part, to its irregular release schedule. We use a model that allows for data sampling at mixed frequencies to analyze the predictive power of the Beige Book. We find that the Beige Book's national summary and District reports predict GDP and aggregate employment and that most District reports provide information content for regional employment. In addition, there appears to be an asymmetry in the predictive content of the Beige Book language. Copyright (c) 2009 The Ohio State University.

Suggested Citation

  • Michelle T. Armesto & Rub…N Hern¡Ndez-Murillo & Michael T. Owyang & Jeremy Piger, 2009. "Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(1), pages 35-55, February.
  • Handle: RePEc:mcb:jmoncb:v:41:y:2009:i:1:p:35-55
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    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Balke, Nathan S & Petersen, D'Ann, 2002. "How Well Does the Beige Book Reflect Economic Activity? Evaluating Qualitative Information Quantitatively," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 114-136, February.
    3. David Fettig & Arthur J. Rolnick & David E. Runkle, 1999. "The Federal Reserve's Beige Book: A better mirror than crystal ball," The Region, Federal Reserve Bank of Minneapolis, vol. 13(Mar), pages 10-13,28-32.
    4. Donna K. Ginther & Madeline Zavodny, 2001. "The Beige Book: Timely information on the regional economy," Economic Review, Federal Reserve Bank of Atlanta, vol. 86(Q3), pages 19-29.
    5. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, vol. 90(3), pages 335-339, March.
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    Cited by:

    1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    2. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    3. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    4. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33.
    5. Elena Andreou & Andros Kourtellos, 2015. "The State and the Future of Cyprus Macroeconomic Forecasting," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 9(1), pages 73-90, June.
    6. Kathryn Lundquist & H.O. Stekler, 2011. "The Forecasting Performance of Business Economists During the Great Recession," Working Papers 2011-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    7. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    8. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(3), pages 584-614.
    9. Paul Gower & Florian Meier & Karl Shutes, 2019. "Regulator Communication and Market Confidence in Difficult Times: Lessons from the Great Financial Crisis," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 7(4), pages 1-24.
    10. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    11. Weber, Christoph S., 2019. "The effect of central bank transparency on exchange rate volatility," Journal of International Money and Finance, Elsevier, vol. 95(C), pages 165-181.
    12. Benjamin Born & Michael Ehrmann & Marcel Fratzscher, 2011. "Macroprudential policy and central bank communication," BIS Papers chapters, in: Bank for International Settlements (ed.),Macroprudential regulation and policy, volume 60, pages 107-110, Bank for International Settlements.
    13. Hamza Bennani & Matthias Neuenkirch, 2017. "The (home) bias of European central bankers: new evidence based on speeches," Applied Economics, Taylor & Francis Journals, vol. 49(11), pages 1114-1131, March.
    14. Stekler, Herman & Symington, Hilary, 2016. "Evaluating qualitative forecasts: The FOMC minutes, 2006–2010," International Journal of Forecasting, Elsevier, vol. 32(2), pages 559-570.
    15. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    16. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    17. J. Scott Davis & Mark A. Wynne, 2016. "Central bank communications: a case study," Globalization Institute Working Papers 283, Federal Reserve Bank of Dallas, revised 01 Sep 2016.
    18. Herman O. Stekler & Hilary Symington, 2014. "How Did The Fomc View The Great Recession As It Was Happening?: Evaluating The Minutes From Fomc Meetings, 2006-2010," Working Papers 2014-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    19. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    20. Sadique, Shibley & In, Francis & Veeraraghavan, Madhu & Wachtel, Paul, 2013. "Soft information and economic activity: Evidence from the Beige Book," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 81-92.
    21. Apel, Mikael & Blix Grimaldi, Marianna, 2012. "The Information Content of Central Bank Minutes," Working Paper Series 261, Sveriges Riksbank (Central Bank of Sweden).
    22. Bruno Deschamps & Christos Ioannidis & Kook Ka, 2019. "High-Frequency Credit Spread Information and Macroeconomic Forecast Revision," Working Papers 2019-17, Economic Research Institute, Bank of Korea.

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