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

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  • Soybilgen, Baris

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.

Suggested Citation

  • Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:94715
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    File URL: https://mpra.ub.uni-muenchen.de/94715/1/MPRA_paper_94715.pdf
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    12. repec:hal:journl:peer-00844811 is not listed on IDEAS
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    More about this item

    Keywords

    Dynamic Factor Model; Neural Network; Recession; Business Cycle;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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