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A neural network approach to inflation forecasting: the case of Italy

Author

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  • Jane M. Binner
  • Alicia M. Gazely

Abstract

In this paper, a Divisia monetary index measure of money is constructed for the Italian economy and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. The powerful and flexible Artificial Intelligence 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 show that superior tracking of inflation is possible for networks that employ a Divisia M2 measure of money. During a period of high financial innovation in Italy Divisia outperforms simple sum at both the AL and M2 levels of monetary aggregation. This support for Divisia is entirely consistent with findings based on standard econometric techniques. Divisia monetary aggregates appear to offer advantages over their simple sum counterparts as macroeconomic indicators. Further, the combination of Divisia measures of money with the artificial neural network offers a promising starting point for improved models of inflation.

Suggested Citation

  • Jane M. Binner & Alicia M. Gazely, 1999. "A neural network approach to inflation forecasting: the case of Italy," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 1(1), pages 76-92.
    • Handle: RePEc:ids:gbusec:v:1:y:1999:i:1:p:76-92
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    Cited by:

    1. 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.
    2. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2004. "Vector autoregressive models versus neural networks in forecasting: an application to Euro-inflation and divisia money," Money Macro and Finance (MMF) Research Group Conference 2003 5, Money Macro and Finance Research Group.

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