Evolution, recurrency and kernels in learning to model inflation

Binner, J.M., Jones, B., Kendall, G., Tino, P. and Tepper, J. (2007). Evolution, recurrency and kernels in learning to model inflation. Working Paper. Aston University, Birmingham (UK).

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. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both.

Divisions: Aston Business School > Economics finance & entrepreneurship
Additional Information: Aston Business School Research Papers are published by the Institute to bring the results of research in progress to a wider audience and to facilitate discussion. They will normally be published in a revised form subsequently and the agreement of the authors should be obtained before referring to its contents in other published works.
Uncontrolled Keywords: Divisia,inflation,evolution strategies,recurrent neural networks,kernel methods
Published Date: 2007-06
Authors: Binner, J.M.
Jones, B.
Kendall, G.
Tino, P.
Tepper, J.

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