Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks

Nobuyuki Kanazawa

Journal of Macroeconomics, 2020, vol. 64, issue C

Abstract: I propose a flexible Radial Basis Functions (RBFs) Artificial Neural Networks method for studying the time series properties of macroeconomic variables. To assess the validity of the RBF approach, 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 NK model from the simulated data of 300 observations. Finally, I apply the RBF estimator to the quarterly US data and show that the positive supply shocks have significantly weaker expansionary effects during the periods of passive monetary policy regimes.

Keywords: Nonlinear vector-autoregression models; Radial basis functions; Zero lower bound; DSGE Models; Supply shocks (search for similar items in EconPapers)
JEL-codes: C45 E31 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:64:y:2020:i:c:s0164070420301361

DOI: 10.1016/j.jmacro.2020.103210

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