A comparison of the forecasting performance of a constructed monetary index with component data using neural networks

A. M. Gazely, J. M. Binner

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Accuracy in the measurement of money is important for economists. The conventional method is to simply sum the various constituent liquid liabilities of banks. This method of arriving at broad money aggregates is seriously flawed and an important alternative is the Divisia weighted index, in which the components are weighted on the basis of the monetary services provided by each. However while there is much evidence in favour of the Divisia index, governments largely continue to work with the 'simple sum' index. This study uses neural networks to demonstrate the superiority, for inflation forecasting purposes, of working directly with the component assets as a first step towards optimising U.K. index construction. The possibility is demonstrated of constructing a new, weighted index based on weights empirically derived from neural network models. An experimental index is shown to have a lower forecasting error than the Divisia index published by the Bank of England.

Original languageEnglish
Title of host publicationProceedings of the 2005 International Conference on Artificial Intelligence, ICAI'05
Pages217-223
Number of pages7
Volume1
Publication statusPublished - 1 Dec 2005
Event2005 International Conference on Artificial Intelligence, ICAI'05 - Las Vegas, NV, United Kingdom
Duration: 27 Jun 200530 Jun 2005

Conference

Conference2005 International Conference on Artificial Intelligence, ICAI'05
CountryUnited Kingdom
CityLas Vegas, NV
Period27/06/0530/06/05

Keywords

  • Divisia
  • Inflation
  • Macroeconomic forecasting
  • Neural networks

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