Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks

Nobuyuki Kanazawa () and 伸幸 金澤

No HIAS-E-64, Discussion paper series from Hitotsubashi Institute for Advanced Study, Hitotsubashi University

Abstract: I propose a flexible nonlinear method for studying the time series properties of macroeconomic variables. In particular, I focus on a class of Artificial Neural Networks (ANN) called the Radial Basis Functions (RBF). To assess the validity of the RBF approach in the macroeconomic time series analysis, I conduct a Monte Carlo experiment using the data generated from a nonlinear New Keynesian (NK) model. I find that the RBF estimator can uncover the structure of the nonlinear NK model from the simulated data whose length is as small as 300 periods. Finally, I apply the RBF estimator to the quarterly US data and show that the response of the macroeconomic variables to a positive supply shock exhibits a substantial time variation. In particular, the positive supply shocks are found to have significantly weaker expansionary effects during the zero lower bound periods as well as periods between 2003 and 2004. The finding is consistent with a basic NK model, which predicts that the higher real interest rate due to the monetary policy inaction weakens the effects of supply shocks.

Keywords: Neural Networks; Radial Basis Functions; Zero Lower Bound; Supply Shocks (search for similar items in EconPapers)
JEL-codes: C45 E31 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2018-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets, nep-mac and nep-ore
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https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/29063/070_hiasDP-E-64.pdf

Related works:
Journal Article: Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hit:hiasdp:hias-e-64

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