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Financial innovation and Divisia monetary indices in Taiwan: a neural network approach


  • Jane M. Binner
  • Alicia M. Gazely
  • Shu-Heng Chen


In this paper a weighted index measure of money using the 'Divisia' formulation is constructed for the Taiwan economy and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. This research extends an earlier study by Gazely and Binner by examining the theory that rapid financial innovation, particularly during the financial liberalization of the 1980s, has been responsible for the poor performance of conventional simple sum monetary aggregates. The Divisia index is adjusted in two ways to allow for the major financial innovations that Taiwan has experienced since the 1970s. The technique of neural networks is used to allow a completely flexible mapping of the variables and a greater variety of functional form than is currently achievable using conventional econometric techniques. Results suggest that superior tracking of inflation is possible for networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money appear to offer advantages over their simple sum counter parts as macroeconomic indicators.

Suggested Citation

  • Jane M. Binner & Alicia M. Gazely & Shu-Heng Chen, 2002. "Financial innovation and Divisia monetary indices in Taiwan: a neural network approach," The European Journal of Finance, Taylor & Francis Journals, vol. 8(2), pages 238-247, June.
  • Handle: RePEc:taf:eurjfi:v:8:y:2002:i:2:p:238-247 DOI: 10.1080/13518470110071173

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    References listed on IDEAS

    1. Drake, Leigh & Mills, Terence C, 2001. "A New Empirical Weighted Monetary Aggregate for the UK," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 217-234, July.
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    3. Diewert, W Erwin, 1978. "Superlative Index Numbers and Consistency in Aggregation," Econometrica, Econometric Society, vol. 46(4), pages 883-900, July.
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    5. Barnett, William A., 1980. "Economic monetary aggregates an application of index number and aggregation theory," Journal of Econometrics, Elsevier, vol. 14(1), pages 11-48, September.
    6. Drake, L. & Mullineux, A., 1995. "One Divisa Money for Europe?," Discussion Papers 95-04, Department of Economics, University of Birmingham.
    7. Benjamin M. Friedman, 1996. "The Rise and Fall of Money Growth Targets as Guidelines for U.S. Monetary Policy," NBER Working Papers 5465, National Bureau of Economic Research, Inc.
    8. Drake, Leigh & Chrystal, K Alec, 1997. "Personal Sector Money Demand in the UK," Oxford Economic Papers, Oxford University Press, vol. 49(2), pages 188-206, April.
    9. Belongia, Michael T, 1996. "Measurement Matters: Recent Results from Monetary Economics Reexamined," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 1065-1083, October.
    10. Drake, Leigh & Fleissig, Adrian R & Mullineux, Andy, 1999. "Are "Risky Assets" Substitutes for "Monetary Assets"?," Economic Inquiry, Western Economic Association International, vol. 37(3), pages 510-526, July.
    11. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    12. Drake, Leigh & Chrystal, K Alec, 1994. "Company-Sector Money Demand: New Evidence on the Existence of a Stable Long-Run Relationship for the United Kingdom," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(3), pages 479-494, August.
    13. A. M. Gazely & J. M. Binner, 2000. "The application of neural networks to the Divisia index debate: evidence from three countries," Applied Economics, Taylor & Francis Journals, vol. 32(12), pages 1607-1615.
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    Cited by:

    1. 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.

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