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Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy

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  • Jiti Gao
  • Fei Liu
  • Bin Peng
  • Yanrong Yang

Abstract

In this paper, we investigate a semiparametric regression model under the context of treatment effects via a localized neural network (LNN) approach. Due to a vast number of parameters involved, we reduce the number of effective parameters by (i) exploring the use of identification restrictions; and (ii) adopting a variable selection method based on the group-LASSO technique. Subsequently, we derive the corresponding estimation theory and propose a dependent wild bootstrap procedure to construct valid inferences accounting for the dependence of data. Finally, we validate our theoretical findings through extensive numerical studies. In an empirical study, we revisit the impacts of a tightening monetary policy action on a variety of economic variables, including short-/long-term interest rate, inflation, unemployment rate, industrial price and equity return via the newly proposed framework using a monthly dataset of the US.

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  • Jiti Gao & Fei Liu & Bin Peng & Yanrong Yang, 2023. "Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy," Papers 2306.05593, arXiv.org, revised Jul 2024.
    • Handle: RePEc:arx:papers:2306.05593
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    References listed on IDEAS

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    1. Kline Patrick & Santos Andres, 2012. "A Score Based Approach to Wild Bootstrap Inference," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 23-41, August.
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    3. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
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