Identifying US business cycle regimes using dynamic factors and neural network models

Baris Soybilgen

MPRA Paper from University Library of Munich, Germany

Abstract: We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Then, dynamic factors are extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in sample and out of sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.

Keywords: Dynamic Factor Model; Neural Network; Recession; Business Cycle (search for similar items in EconPapers)
JEL-codes: C38 E32 E37 (search for similar items in EconPapers)
Date: 2018-07-05
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:94715

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