Does Money Matter? An Artificial Intelligence Approach
Jane Binner 0, Barry Jones, Graham Kendall, Jonathan Tepper, Peter Tino
0Aston Business School
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.128How to use a DOI?
- Divisia money, Artificial Intelligence, inflation
- This paper provides the most complete evidence to date on the importance of monetary aggregates as a policy tool in an inflation forecasting experiment. Every possible definition of ‘money’ in the USA is being considered for the full data period (1960 – 2006), using the most sophisticated non-linear artificial intelligence techniques available, namely, recurrent neural networks, evolutionary strategies and kernel methods. Three top computer Dr Peter Tino at the University of Birmingham, Dr Graham Kendall at the University of Nottingham and Dr Jonathan Tepper at Nottingham Trent University, are competing to find the best fitting US inflation forecasting models using their own specialist artificial intelligence techniques. Results will be evaluated using standard forecasting evaluation criteria and compared to forecasts from traditional econometric models produced by Dr Binner. This paper therefore addresses not only the most controversial questions in monetary economics - exactly how to construct monetary aggregates and to what level of aggregation, but also addresses the ever increasing role of artificial intelligence techniques in economics and how these methods can improve upon traditional econometric modelling techniques. Given the multidisciplinary nature of this work, the results will also add value to the existing knowledge of computer scientists in particular and more generally speaking, any scientist using artificial intelligence techniques.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jane Binner AU - Barry Jones AU - Graham Kendall AU - Jonathan Tepper AU - Peter Tino PY - 2006/10 DA - 2006/10 TI - Does Money Matter? An Artificial Intelligence Approach PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.128 DO - https://doi.org/10.2991/jcis.2006.128 ID - Binner2006/10 ER -