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

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Cited by:

  1. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
  2. 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, April.
  3. 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.
  4. J. Scott Davis & Mark A. Wynne, 2016. "Central bank communications: a case study," Globalization Institute Working Papers 283, Federal Reserve Bank of Dallas.
  5. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Pérez & Elena Vidal, 2022. "Using newspapers for textual indicators: which and how many?," Working Papers 2235, Banco de España.
  6. 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.
  7. Joel Elvery, 2024. "Introduction to the Cleveland Fed Survey of Regional Conditions and Expectations (SORCE) Indexes," Cleveland Fed District Data Brief 99167, Federal Reserve Bank of Cleveland.
  8. 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.
  9. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Perez & Elena Vidal, 2024. "Using Newspapers for Textual Indicators: Guidance Based on Spanish- and Portuguese-Speaking Countries," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 643-692, August.
  10. 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.
  11. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
  12. 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.
  13. Stekler, Herman & Symington, Hilary, 2016. "Evaluating qualitative forecasts: The FOMC minutes, 2006–2010," International Journal of Forecasting, Elsevier, vol. 32(2), pages 559-570.
  14. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
  15. Ghysels, Eric & Ball, Ryan & Zhou, Huan, 2014. "Can we Automate Earnings Forecasts and Beat Analysts?," CEPR Discussion Papers 10186, C.E.P.R. Discussion Papers.
  16. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
  17. 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.
  18. 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.
  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. Charles S. Gascon & Devin Werner, 2022. "Does the Beige Book Reflect U.S. Employment and Inflation Trends?," Economic Synopses, Federal Reserve Bank of St. Louis, issue 13, pages 1-3, June.
  21. Huang, Yu-Lieh & Kuan, Chung-Ming, 2021. "Economic prediction with the FOMC minutes: An application of text mining," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 751-761.
  22. Apel, Mikael & Blix Grimaldi, Marianna, 2012. "The Information Content of Central Bank Minutes," Working Paper Series 261, Sveriges Riksbank (Central Bank of Sweden).
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. Amrendra Pandey & Jagadish Shettigar & Amarnath Bose, 2021. "Evaluation of the Inflation Forecasting Process of the Reserve Bank of India: A Text Analysis Approach," SAGE Open, , vol. 11(3), pages 21582440211, July.
  28. 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.
  29. 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.
  30. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
  31. 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.
  32. repec:fip:fedcwq:98080 is not listed on IDEAS
  33. Ghysels, Eric & Ball, Ryan, 2017. "Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer?," CEPR Discussion Papers 12179, C.E.P.R. Discussion Papers.
  34. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
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