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Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

In: Financial Aggregation And Index Number Theory

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  • William A. Barnett

    (University of Kansas, USA)

  • Marcelle Chauvet

    (University of California at Riverside, USA)

  • Heather L. R. Tierney

Abstract

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases — a couple of quarters before the beginning of recessions — and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interestrate phases. We note the inferences' policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia (1996) has observed in this regard, "measurement matters."

Suggested Citation

  • 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..
  • Handle: RePEc:wsi:wschap:9789814293105_0007
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    1. William A. Barnett, 2011. "Multilateral Aggregation-Theoretic Monetary Aggregation over Heterogeneous Countries," World Scientific Book Chapters, in: Financial Aggregation And Index Number Theory, chapter 6, pages 167-206, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

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    2. Choi-Meng Leong & Chin-Hong Puah & Shazali Abu Mansor & Evan Lau, 2010. "Testing the Effectiveness of Monetary Policy in Malaysia Using Alternative Monetary Aggregation," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(3), pages 321-338, August.
    3. Jawadi, Fredj & Sousa, Ricardo M., 2013. "Money demand in the euro area, the US and the UK: Assessing the role of nonlinearity," Economic Modelling, Elsevier, vol. 32(C), pages 507-515.
    4. Chin-Hong, Puah & Lee-Chea, Hiew, 2010. "Financial Liberalization, Weighted Monetary Aggregates and Money Demand in Indonesia," MPRA Paper 31731, University Library of Munich, Germany.
    5. 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.
    6. Ryan S. Mattson & Philippe de Peretti, 2014. "Investigating the Role of Real Divisia Money in Persistence-Robust Econometric Models," Working Papers hal-00984827, HAL.
    7. Fleissig, Adrian R. & Jones, Barry E., 2015. "The impact of commercial sweeping on the demand for monetary assets during the Great Recession," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 412-422.
    8. Barnett, William A. & Chauvet, Marcelle, 2008. "The End of the Great Moderation: “We told you so.”," MPRA Paper 11642, University Library of Munich, Germany.
    9. Ellington, Michael, 2018. "The case for Divisia monetary statistics: A Bayesian time-varying approach," Journal of Economic Dynamics and Control, Elsevier, vol. 96(C), pages 26-41.
    10. 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..
    11. 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.
    12. Hachmi Ben Ameur & Fredj Jawadi & Abdoulkarim Idi Cheffou & Wael Louhichi, 2018. "Measurement errors in stock markets," Annals of Operations Research, Springer, vol. 262(2), pages 287-306, March.
    13. Fredj Jawadi & Ricardo M. Sousa, 2012. "Consumption and Wealth in the US, the UK and the Euro Area:A Nonlinear Investigation," NIPE Working Papers 24/2012, NIPE - Universidade do Minho.

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    More about this item

    Keywords

    Index Number Theory; Aggregation Theory; Money; Financial Assets; Monetary Aggregates;
    All these keywords.

    JEL classification:

    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates

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