The roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data

Ji Eun Choi, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by Mc- Cracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

Original languageEnglish
Pages (from-to)497-506
Number of pages10
JournalCommunications for Statistical Applications and Methods
Volume26
Issue number5
DOIs
StatePublished - 2019

Keywords

  • Deep neural network
  • Differencing
  • Dimension reduction
  • Employment forecast
  • Gated recurrent unit
  • Long short term memory

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