Does money matter in inflation forecasting?.

Binner, J, Tino, P, Tepper, J ORCID: https://orcid.org/0000-0001-7339-0132, Andersen, R, Jones, B and Kendall, G, 2010. Does money matter in inflation forecasting?. Physica A - Statistical Mechanics and its Applications, 389 (21), pp. 4793-4808.

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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 non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques 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 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.

Item Type:	Journal article
Publication Title:	Physica A - Statistical Mechanics and its Applications
Creators:	Binner, J., Tino, P., Tepper, J., Andersen, R., Jones, B. and Kendall, G.
Publisher:	Elsevier (not including Cell Press)
Date:	2010
Volume:	389
Number:	21
Identifiers:	Number Type 10.1016/j.physa.2010.06.015 DOI
Rights:	Crown copyright © 2010 Published by Elsevier B.V.
Divisions:	Schools > School of Science and Technology
Record created by:	EPrints Services
Date Added:	09 Oct 2015 10:02
Last Modified:	04 Feb 2022 12:23
URI:	https://irep.ntu.ac.uk/id/eprint/6800

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