IDEAS home Printed from https://ideas.repec.org/a/fip/fedfel/00098.html

Do macro variables help forecast interest rates?

Author

Abstract

Some recent research has suggested that macroeconomic variables, such as output and inflation, can improve interest rate forecasts. However, the evidence for this puzzling result is based on unreliable statistical tests. A new simple method more reliably assesses which variables are useful for forecasting. The results from this method suggest that some of the published evidence on the predictive power of macroeconomic variables may be spurious, supporting the more traditional view that current interest rates contain all the relevant information for predicting future interest rates.

Suggested Citation

  • Michael D. Bauer & James D. Hamilton, 2016. "Do macro variables help forecast interest rates?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfel:00098
    as

    Download full text from publisher

    File URL: http://www.frbsf.org/economic-research/files/el2016-20.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    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. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2023. "Random Subspace Local Projections," CAMA Working Papers 2023-34, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Jannsen, Nils & Wolters, Maik H., 2016. "Zu Produktionspotenzial und Produktionslücke in den Vereinigten Staaten," Kiel Insight 2016.2, Kiel Institute for the World Economy.
    4. Vincent Brémond & Emmanuel Hache & Tovonony Razafindrabe, 2016. "The Oil Price and Exchange Rate Relationship Revisited: A time-varying VAR parameter approach," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 13(1), pages 97-131, June.
    5. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    6. 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.
    7. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    8. Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2021. "Time-varying influence of household debt on inequality in United Kingdom," Empirical Economics, Springer, vol. 61(4), pages 1917-1933, October.
    9. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    10. Afees A. Salisu & Umar B. Ndako, 2017. "A new look at the stock price-exchange rate nexus," Working Papers 031, Centre for Econometric and Allied Research, University of Ibadan.
    11. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    12. Janet L. Yellen, 2015. "Inflation Dynamics and Monetary Policy : A speech at the Philip Gamble Memorial Lecture, University of Massachusetts, Amherst, Amherst, Massachusetts, September 24, 2015," Speech 863, Board of Governors of the Federal Reserve System (U.S.).
    13. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
    14. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    15. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    16. Nonejad, Nima, 2021. "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, vol. 41(C).
    17. Rahul Deb & Mallesh M. Pai & Maher Said, 2018. "Evaluating Strategic Forecasters," American Economic Review, American Economic Association, vol. 108(10), pages 3057-3103, October.
    18. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    19. Apergis, Nicholas & Gupta, Rangan, 2017. "Can (unusual) weather conditions in New York predict South African stock returns?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 377-386.
    20. Reslow, André, 2019. "Inefficient Use of Competitors'Forecasts?," Working Paper Series 380, Sveriges Riksbank (Central Bank of Sweden).

    More about this item

    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:fip:fedfel:00098. 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: Federal Reserve Bank of San Francisco Research Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbsfus.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.