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Forecasting broad money velocity

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  • 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
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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. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    3. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    4. 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.
    5. Friedman, Milton, 1988. "Money and the Stock Market," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 221-245, April.
    6. 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.
    7. 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.
    8. 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.
    9. Laidler, David, 1990. "Understanding velocity: New approaches and their policy relevance--Introduction," Journal of Policy Modeling, Elsevier, vol. 12(2), pages 141-163.
    10. 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.
    11. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    17. 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.
    18. 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.
    19. 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.
    20. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    21. Dr. Samuel Reynard, 2006. "Money and the Great Disinflation," Working Papers 2006-07, Swiss National Bank.
    22. Christian Bordes & Laurent Clerc & Vêlayoudom Marimoutou, 2007. "Is there a structural break in equilibrium velocity in the euro area?," Post-Print hal-00308654, HAL.
    23. 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.
    24. 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.
    25. 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.
    26. 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.).
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
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    Cited by:

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    2. 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.
    3. 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.
    4. 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.
    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.
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    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

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