IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v42y2017icp421-432.html
   My bibliography  Save this article

Forecasting broad money velocity

Author

Listed:
  • Jung, Alexander

Abstract

This paper applies traditional approaches and mixed-data sampling (MIDAS) to explain and forecast velocity of broad money in the euro area and the United States. Our results show that despite financial innovations, over the last two decades broad money velocity followed a declining trend with one break around the start of the financial crisis in both economies. A new result is that applying mixed-frequency techniques, we find improvements in velocity forecasts for the euro area at all horizons considered (one to eight quarters ahead), whereas for the US possible gains only refer to shorter-term forecasts.

Suggested Citation

  • Jung, Alexander, 2017. "Forecasting broad money velocity," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 421-432.
    • Handle: RePEc:eee:ecofin:v:42:y:2017:i:c:p:421-432
    DOI: 10.1016/j.najef.2017.08.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940816301851
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2017.08.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Orphanides, Athanasios & Porter, Richard D., 2000. "P revisited: money-based inflation forecasts with a changing equilibrium velocity," Journal of Economics and Business, Elsevier, vol. 52(1-2), pages 87-100.
    3. Friedman, Milton, 1988. "Money and the Stock Market," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 221-245, April.
    4. Anderson, Richard G. & Bordo, Michael & Duca, John V., 2017. "Money and velocity during financial crises: From the great depression to the great recession," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 32-49.
    5. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    6. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    7. Christian Dreger & Jürgen Wolters, 2009. "Money velocity and asset prices in the euro area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(1), pages 51-63, February.
    8. Lucas, Robert E., 1988. "Money demand in the United States: A quantitative review," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 29(1), pages 137-167, January.
    9. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    12. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    13. Dr. Samuel Reynard, 2006. "Money and the Great Disinflation," Working Papers 2006-07, Swiss National Bank.
    14. Ruth A. Judson & Bernd Schlusche & Vivian Wong, 2014. "Demand for M2 at the Zero Lower Bound: The Recent U.S. Experience," Finance and Economics Discussion Series 2014-22, Board of Governors of the Federal Reserve System (U.S.).
    15. George R. Moore & Richard D. Porter & David H. Small, 1990. "Modeling the disaggregated demands for M2 and M1: the U.S. experience in the 1980s," Proceedings, Board of Governors of the Federal Reserve System (U.S.), pages 21-112.
    16. Claus Brand & Dieter Gerdesmeier & Barbara Roffia, 2002. "Estimating the trend of M3 income velocity underlying the reference value for monetary growth," Occasional Paper Series 03, European Central Bank.
    17. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    18. Karl Brunner & Allan H. Meltzer, 1963. "Predicting Velocity: Implications For Theory And Policy," Journal of Finance, American Finance Association, vol. 18(2), pages 319-354, May.
    19. Dreger, Christian & Wolters, Jürgen, 2014. "Money demand and the role of monetary indicators in forecasting euro area inflation," International Journal of Forecasting, Elsevier, vol. 30(2), pages 303-312.
    20. 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.
    21. Laidler, David, 1990. "Understanding velocity: New approaches and their policy relevance--Introduction," Journal of Policy Modeling, Elsevier, vol. 12(2), pages 141-163.
    22. El-Shagi, Makram & Giesen, Sebastian & Kelly, Logan J., 2015. "The Quantity Theory Revisited: A New Structural Approach," Macroeconomic Dynamics, Cambridge University Press, vol. 19(1), pages 58-78, January.
    23. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    24. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    25. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    26. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    27. Mr. Joaquim Vieira Ferreira Levy & Mr. Alessandro Calza & Mr. Dieter Gerdesmeier, 2001. "Euro Area Money Demand: Measuring the Opportunity Costs Appropriately," IMF Working Papers 2001/179, International Monetary Fund.
    28. Christian Bordes & Laurent Clerc & Vêlayoudom Marimoutou, 2007. "Is there a structural break in equilibrium velocity in the euro area?," Post-Print hal-00308654, HAL.
    29. Brand, Claus & Gerdesmeier, Dieter & Roffia, Barbara, 2002. "Estimating the trend of M3 income velocity underlying the reference value for monetary growth," Occasional Paper Series 3, European Central Bank.
    30. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    31. Fischer, B. & Lenza, M. & Pill, H. & Reichlin, L., 2009. "Monetary analysis and monetary policy in the euro area 1999-2006," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1138-1164, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Afees A. Salisu & Rangan Gupta, 2021. "How Do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4286-4311, December.
    2. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    3. Jung, Alexander & Carcel Villanova, Hector, 2020. "The empirical properties of euro area M3, 1980-2017," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 37-49.
    4. Afees A. Salisu & Rangan Gupta & Riza Demirer, 2022. "A Note On Uncertainty Due To Infectious Diseases And Output Growth Of The United States: A Mixed-Frequency Forecasting Experiment," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-9, June.
    5. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    6. Susan Sunila Sharma & Ferry Syarifuddin, 2019. "Determinants Of Indonesia’S Income Velocity Of Money," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 21(3), pages 323-342, January.
    7. repec:idn:journl:v:21:y:2019:i:3c:p:1-20 is not listed 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. Jung, Alexander & Carcel Villanova, Hector, 2020. "The empirical properties of euro area M3, 1980-2017," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 37-49.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    4. 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.
    5. Christian Dreger & Dieter Gerdesmeier & Barbara Roffia, 2019. "Re‐vitalizing money demand in the Euro area. Still valid at the zero‐lower bound," Bulletin of Economic Research, Wiley Blackwell, vol. 71(4), pages 599-615, October.
    6. Beyer, Andreas, 2009. "A Stable Model for Euro Area Money Demand: Revisiting the Role of Wealth," Working Paper Series 1111, European Central Bank.
    7. Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
    8. 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.
    9. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    10. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    11. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    12. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
    13. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    14. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    15. Rudan Wang & Bruce Morley & Javier Ordóñez, 2016. "The Taylor Rule, Wealth Effects and the Exchange Rate," Review of International Economics, Wiley Blackwell, vol. 24(2), pages 282-301, May.
    16. Chen, Yu-chin & Turnovsky, Stephen J. & Zivot, Eric, 2014. "Forecasting inflation using commodity price aggregates," Journal of Econometrics, Elsevier, vol. 183(1), pages 117-134.
    17. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    18. 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.
    19. 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.
    20. Heimonen, Kari, 2010. "Money and equity returns in the Euro area," Global Finance Journal, Elsevier, vol. 21(2), pages 152-169.

    More about this item

    Keywords

    Money velocity; Mixed frequency; United States; Euro area;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

    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:eee:ecofin:v:42:y:2017:i:c:p:421-432. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    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.