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The Relative Forecasting Performance of the Divisia and Simple Sum Monetary Aggregates

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  • Schunk, Donald L

Abstract

This paper provides direct evidence on the forecasting performance of the divisia monetary aggregates relative to the traditional simple sum monetary aggregates. It is shown that forecasts of U.S. real GDP from a four variable vector autoregression (VAR) are most accurate when a divisia aggregate is used rather than a simple sum aggregate, particularly at broad levels of aggregation. Further, the two M1 aggregates, relative to the broader aggregates, are superior predictors of the GDP deflator, with a slight edge going to divisia M1 over simple sum M1.

Suggested Citation

  • Schunk, Donald L, 2001. "The Relative Forecasting Performance of the Divisia and Simple Sum Monetary Aggregates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(2), pages 272-283, May.
  • Handle: RePEc:mcb:jmoncb:v:33:y:2001:i:2:p:272-83
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    Cited by:

    1. William A. Barnett & Unja Chae & John W. Keating, 2011. "The Discounted Economic Stock of Money with VAR Forecasting," World Scientific Book Chapters,in: Financial Aggregation And Index Number Theory, chapter 4, pages 107-150 World Scientific Publishing Co. Pte. Ltd..
    2. William A. Barnett & Marcelle Chauvet & Heather L. R. Tierney, 2011. "Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach," World Scientific Book Chapters,in: Financial Aggregation And Index Number Theory, chapter 7, pages 207-249 World Scientific Publishing Co. Pte. Ltd..
    3. Seitz, Franz & Baumann, Ursel & Albuquerque, Bruno, 2015. "The information content of money and credit for US activity," Working Paper Series 1803, European Central Bank.
    4. Xian HUANG & Shilong XIA, 2015. "Currency - Equivalent Vs . Divisia Monetary Aggregates: Theoretical Evaluation And Empirical Evidence From The United States And China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 60-80, September.
    5. Alicia Gazely & Jane Binner & Graham Kendall, 2004. "Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money," Computing in Economics and Finance 2004 258, Society for Computational Economics.
    6. Richard G. Anderson & Marcelle Chauvet & Barry Jones, 2015. "Nonlinear Relationship Between Permanent and Transitory Components of Monetary Aggregates and the Economy," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 228-254, February.
    7. Dahalan, Jauhari & Sharma, Subhash C. & Sylwester, Kevin, 2005. "Divisia monetary aggregates and money demand for Malaysia," Journal of Asian Economics, Elsevier, vol. 15(6), pages 1137-1153, January.
    8. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach," Economics Bulletin, AccessEcon, vol. 33(2), pages 1101-1115.
    9. William A. Barnett & Soumya Suvra Bhadury & Taniya Ghosh, 2016. "An SVAR Approach to Evaluation of Monetary Policy in India: Solution to the Exchange Rate Puzzles in an Open Economy," Open Economies Review, Springer, vol. 27(5), pages 871-893, November.
    10. Elger, Thomas & Jones, Barry E. & Nilsson, Birger, 2006. "Forecasting with Monetary Aggregates: Recent Evidence for the United States," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 428-446.
    11. Barnett, William A. & Chauvet, Marcelle, 2008. "The End of the Great Moderation: “We told you so.”," MPRA Paper 11642, University Library of Munich, Germany.
    12. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    13. Elger, C. Thomas & Jones, Barry E. & Edgerton, David L. & Binner, Jane M., 2008. "A Note On The Optimal Level Of Monetary Aggregation In The United Kingdom," Macroeconomic Dynamics, Cambridge University Press, vol. 12(01), pages 117-131, February.
    14. Binner, Jane M. & Bissoondeeal, Rakesh K. & Elger, C. Thomas & Jones, Barry E. & Mullineux, Andrew W., 2009. "Admissible monetary aggregates for the euro area," Journal of International Money and Finance, Elsevier, vol. 28(1), pages 99-114, February.
    15. William A. Barnett & Marcelle Chauvet, 2011. "International Financial Aggregation and Index Number Theory: A Chronological Half-Century Empirical Overview," World Scientific Book Chapters,in: Financial Aggregation And Index Number Theory, chapter 1, pages 1-51 World Scientific Publishing Co. Pte. Ltd..
    16. Barnett, William A. & Chauvet, Marcelle, 2011. "How better monetary statistics could have signaled the financial crisis," Journal of Econometrics, Elsevier, vol. 161(1), pages 6-23, March.
    17. Binner, Jane & Elger, Thomas, 2002. "The UK Personal Sector Demand for Risky Money," Working Papers 2002:9, Lund University, Department of Economics.
    18. Duca, John V. & VanHoose, David D., 2004. "Recent developments in understanding the demand for money," Journal of Economics and Business, Elsevier, vol. 56(4), pages 247-272.
    19. Binner, J.M. & Tino, P. & Tepper, J. & Anderson, R. & Jones, B. & Kendall, G., 2010. "Does money matter in inflation forecasting?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4793-4808.
    20. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    21. Wenjuan Chen & Dieter Nautz, 2015. "The Information Content of Monetary Statistics for the Great Recession: Evidence from Germany," SFB 649 Discussion Papers SFB649DP2015-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    22. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    23. repec:ecb:ecbwps:20141803 is not listed on IDEAS

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