IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/26872.html
   My bibliography  Save this paper

FRED-QD: A Quarterly Database for Macroeconomic Research

Author

Listed:
  • Michael McCracken
  • Serena Ng

Abstract

In this paper we present and describe a large quarterly frequency, macroeconomic database. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD, our goal is simply to provide a publicly available source of macroeconomic “big data” that is updated in real time using the FRED database. We show that factors extracted from this data set exhibit similar behavior to those extracted from the original Stock and Watson data set. The dominant factors are shown to be insensitive to outliers, but outliers do affect the relative influence of the series as indicated by leverage scores. We then investigate the role unit root tests play in the choice of transformation codes with an emphasis on identifying instances in which the unit root-based codes differ from those already used in the literature. Finally, we show that factors extracted from our data set are useful for forecasting a range of macroeconomic series and that the choice of transformation codes can contribute substantially to the accuracy of these forecasts.

Suggested Citation

  • Michael McCracken & Serena Ng, 2020. "FRED-QD: A Quarterly Database for Macroeconomic Research," NBER Working Papers 26872, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26872
    Note: EFG ME
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w26872.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Piazzesi, M. & Schneider, M., 2016. "Housing and Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1547-1640, Elsevier.
    2. Guerrieri, V. & Uhlig, H., 2016. "Housing and Credit Markets," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1427-1496, Elsevier.
    3. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
    4. Davide Melcangi, 2024. "Firms' Precautionary Savings and Employment during a Credit Crisis," American Economic Journal: Macroeconomics, American Economic Association, vol. 16(1), pages 356-386, January.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Jushan Bai & Serena Ng, 2021. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1746-1763, October.
    7. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "Varieties of Crises and Their Dates," Introductory Chapters, in: This Time Is Different: Eight Centuries of Financial Folly, Princeton University Press.
    8. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393, Elsevier.
    9. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    10. Markus K. Brunnermeier, 2009. "Deciphering the Liquidity and Credit Crunch 2007-2008," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 77-100, Winter.
    11. S. J. Koopman & G. Mesters, 2017. "Empirical Bayes Methods for Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 486-498, July.
    12. Giovanni Angelini & Emanuele Bacchiocchi & Giovanni Caggiano & Luca Fanelli, 2019. "Uncertainty across volatility regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 437-455, April.
    13. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-07, Economic Statistics Centre of Excellence (ESCoE).
    14. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    15. Mark Gertler & Nobuhiro Kiyotaki, 2015. "Banking, Liquidity, and Bank Runs in an Infinite Horizon Economy," American Economic Review, American Economic Association, vol. 105(7), pages 2011-2043, July.
    16. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    17. J. B. Taylor & Harald Uhlig (ed.), 2016. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 2, number 2.
    18. Brinca, P. & Chari, V.V. & Kehoe, P.J. & McGrattan, E., 2016. "Accounting for Business Cycles," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1013-1063, Elsevier.
    19. Sílvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2017. "Bootstrap Prediction Intervals for Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 53-69, January.
    20. Diebold, Francis X & Kilian, Lutz, 2000. "Unit-Root Tests Are Useful for Selecting Forecasting Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 265-273, July.
    21. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    22. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    23. King, Robert G. & Rebelo, Sergio T., 1999. "Resuscitating real business cycles," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 14, pages 927-1007, Elsevier.
    24. Gertler, Mark & Kiyotaki, Nobuhiro & Queralto, Albert, 2012. "Financial crises, bank risk exposure and government financial policy," Journal of Monetary Economics, Elsevier, vol. 59(S), pages 17-34.
    25. Stock, James H, 1996. "VAR, Error Correction and Pretest Forecasts at Long Horizons," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 685-701, November.
    26. Alessandro Barbarino & Efstathia Bura, 2017. "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series 2017-004, Board of Governors of the Federal Reserve System (U.S.).
    27. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 541-559, October.
    28. Altinkilic, Oya & Hansen, Robert S, 2000. "Are There Economies of Scale in Underwriting Fees? Evidence of Rising External Financing Costs," The Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 191-218.
    29. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    30. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    31. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sriya Anbil & Mark A. Carlson & Christopher Hanes & David C. Wheelock, 2021. "A New Daily Federal Funds Rate Series and History of the Federal Funds Market, 1928-54," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 45-70, January.
    2. Kieran Larkin, 2021. "Financial Shocks or Productivity Slowdown: Contrasting the Great Recession and Recovery in the United States and United Kingdom," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 99-126, January.
    3. Kevin L. Kliesen & David C. Wheelock, 2021. "Managing a New Policy Framework: Paul Volcker, the St. Louis Fed, and the 1979-82 War on Inflation," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 71-97, January.
    4. Leung, Charles Ka Yui & Ng, Joe Cho Yiu, 2018. "Macro Aspects of Housing," MPRA Paper 93512, University Library of Munich, Germany.
    5. Keiichiro Kobayashi & Tomoyuki Nakajima, 2014. "A macroeconomic model of liquidity crises," KIER Working Papers 876, Kyoto University, Institute of Economic Research.
    6. Clancy, Daragh & Merola, Rossana, 2017. "Countercyclical capital rules for small open economies," Journal of Macroeconomics, Elsevier, vol. 54(PB), pages 332-351.
    7. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    8. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    9. Mark Gertler & Nobuhiro Kiyotaki & Andrea Prestipino, 2020. "A Macroeconomic Model with Financial Panics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(1), pages 240-288.
    10. Millard, Stephen & Varadi, Alexandra & Yashiv, Eran, 2018. "Shock transmission and the interaction of financial and hiring frictions," Bank of England working papers 769, Bank of England.
    11. Lawrence J. Christiano & Martin S. Eichenbaum & Mathias Trabandt, 2018. "On DSGE Models," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 113-140, Summer.
    12. Queralto, Albert, 2020. "A model of slow recoveries from financial crises," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 1-25.
    13. Alberto Martin & Enrique Moral Benito & Tom Schmitz, 2018. "The financial transmission of housing bubbles: evidence from spain," 2018 Meeting Papers 299, Society for Economic Dynamics.
    14. Keiichiro KOBAYASHI & Tomoyuki NAKAJIMA & Shuhei TAKAHASHI, 2020. "Lack of debt restructuring and lender's credibility - A theory of nonperforming loans -," CIGS Working Paper Series 20-002E, The Canon Institute for Global Studies.
    15. Mark Gertler & Nobuhiro Kiyotaki & Andrea Prestipino, 2020. "Credit Booms, Financial Crises and Macroprudential Policy," Working Papers 2020-62, Princeton University. Economics Department..
    16. Ozhan, Galip Kemal, 2020. "Financial intermediation, resource allocation, and macroeconomic interdependence," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 265-278.
    17. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    18. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    19. Keiichiro Kobayashi & Daichi Shirai, 2017. "Debt-Ridden Borrowers and Economic Slowdown," CIGS Working Paper Series 17-002E, The Canon Institute for Global Studies.
    20. Urban, Jörg, 2020. "Credit cycles revisited," Working Paper Series in Economics 146, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.

    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

    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:nbr:nberwo:26872. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.