Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

A Conversation article celebrating Pi
http://bit.ly/1DXuXbV
What do we spend so much in supermarkets?
http://bit.ly/1yW6If7

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Non-symmetric Vehicle Routing

Publication(s)

Scripting the Game of Lemmings with a Genetic Algorithm
http://bit.ly/g0igy0
Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling
http://bit.ly/h17mwh
Choice Function and Random HyperHeuristics
http://bit.ly/e7QYog
Using an Evolutionary Algorithm for the Tuning of a Chess Evaluation Function Based on a Dynamic Boundary Strategy
http://bit.ly/hsgyZ8

Graham Kendall: Details of Requested Publication


Citation

Binner, J.M; Tino, P; Tepper, J; Anderson, R; Jones, B and Kendall, G Does money matter in inflation forecasting?. Physica A: Statistical Mechanics and its Applications, 389 (21): 4793-4808, 2010.


Abstract

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a na´ve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.


pdf

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doi

The doi for this publication is 10.1016/j.physa.2010.06.015 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2013 (1.722), 2012 (1.676), 2011 (1.373), 2010 (1.521), 2009 (1.562), 2008 (1.441), 2007 (1.430), 2006 (1.311), 2005 (1.332), 2004 (1.369), 2003 (1.180), 2002 (1.369), 2001 (1.295), 2000 (1.205)

URL

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Bibtex

@ARTICLE{bttajk2010, author = {J.M. Binner and P. Tino and J. Tepper and R. Anderson and B. Jones and G. Kendall},
title = {Does money matter in inflation forecasting?},
journal = {Physica A: Statistical Mechanics and its Applications},
year = {2010},
volume = {389},
pages = {4793-4808},
number = {21},
month = {November 2010},
abstract = {This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a na´ve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.},
doi = {10.1016/j.physa.2010.06.015},
issn = {0378-4371},
keywords = {Inflation, Monetary aggregates, Recurrent neural networks, kernel methods},
owner = {gxk},
timestamp = {2010.10.12},
webpdf = {http://www.graham-kendall.com/papers/bttajk2010.pdf} }