Does money matter in inflation forecasting?
Does money matter in inflation forecasting?
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DOI：
10.1016/j.physa.2010.06.015
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发表时间：
2010-11-01
影响因子：
3.3
通讯作者：
Kendall, G.
中科院分区：
文献类型：
--
作者：
Binner, J. M.;Tino, P.;Kendall, G.
本文提供了迄今为止最全面的证据，证明货币总量是否对预测2000年代初至中期的美国通胀有价值。我们探讨了货币的各种不同定义，包括不同的汇总方法和不同的货币资产集合。在我们的预测实验中，我们使用两个非线性技术，即递归神经网络和核递归最小二乘回归技术，是新的宏观经济学。递归神经网络的输入记忆可能是无限的，而核回归技术是一个有限的记忆预测器。这两种方法竞争，以找到最合适的美国通货膨胀预测模型，然后比较从一个天真的随机游走模型的预测。最好的模型是基于核方法的非线性自回归模型。我们的研究结果并没有为货币总量在预测通货膨胀方面的有用性提供太多支持。除了其经济研究结果，我们的研究是在传统的物理学家的长期兴趣之间的相互联系统计力学，神经网络和相关的非参数统计方法，并提出了潜在的途径，这些研究的扩展。皇冠版权所有（C）2010由爱思唯尔B.V.出版保留所有权利。
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 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 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. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.