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Financial Innovation and Divisia Money in Taiwan: Comparative Evidence from Neural Network and Vector Error-Correction Forecasting Models


  • Jane M. Binner
  • Alicia M. Gazely
  • Shu-Heng Chen
  • Bin-Tzong Chie


In this article a Divisia monetary index is constructed for the Taiwan economy, and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. The Divisia index is adjusted in two ways to allow for the financial liberalization that Taiwan has experienced since the 1970s. The powerful artificial intelligence technique of neural networks is used and is found to beat the conventional econometric techniques in a simple inflation forecasting experiment. The preferred inflation forecasting model is achieved using 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. The explanatory power of the two innovation-adjusted Divisia aggregates dominates that of the simple sum counterpart in the majority of cases. (JEL "C4", "E4", "E5") Copyright 2004 Western Economic Association International.

Suggested Citation

  • Jane M. Binner & Alicia M. Gazely & Shu-Heng Chen & Bin-Tzong Chie, 2004. "Financial Innovation and Divisia Money in Taiwan: Comparative Evidence from Neural Network and Vector Error-Correction Forecasting Models," Contemporary Economic Policy, Western Economic Association International, vol. 22(2), pages 213-224, April.
  • Handle: RePEc:bla:coecpo:v:22:y:2004:i:2:p:213-224

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    References listed on IDEAS

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    10. Ford, J.L., 1997. "Output, the Price Level, Broad Money, and Divisia Monetary Aggregates With and Without Innovation: Taiwan, 1967(1)-1995(4)," Discussion Papers 97-17, Department of Economics, University of Birmingham.
    11. Swofford, James L & Whitney, Gerald A, 1988. "Comparison of Nonparametric Tests of Weak Separability for Annual and Quarterly Data on Consumption, Leisure, and Money," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 241-246, April.
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    14. 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. Rana Ejaz Ali Khan & Qazi Muhammad Adnan Hye, 2013. "Financial liberalization and demand for money: a case of Pakistan," Journal of Developing Areas, Tennessee State University, College of Business, vol. 47(2), pages 175-198, July-Dece.
    2. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    3. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    4. repec:bla:manchs:v:85:y:2017:i::p:104-120 is not listed on IDEAS
    5. James, Gregory A., 2005. "Money demand and financial liberalization in Indonesia," Journal of Asian Economics, Elsevier, vol. 16(5), pages 817-829, October.

    More about this item

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit


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