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

Jiti Gao, Fei Liu, Bin Peng and Yanrong Yang

No 14/24, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: Dependent Wild Bootstrap; Group-LASSO; Semiparametric Model; Treatment Effects (search for similar items in EconPapers)
JEL-codes: C14 C22 C45 (search for similar items in EconPapers)
Pages: Â 81
Date: 2024
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp and nep-mon
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Working Paper: Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy (2024)
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