The roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data
| Autor: | Ji Eun Choi, Dong Wan Shin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Computer science Big data Machine learning computer.software_genre 01 natural sciences Unit (housing) 010104 statistics & probability 0502 economics and business 0101 mathematics 050205 econometrics Series (mathematics) Artificial neural network business.industry Applied Mathematics Dimensionality reduction 05 social sciences Federal Reserve Economic Data Economic statistics Autoregressive model Modeling and Simulation Artificial intelligence Statistics Probability and Uncertainty business computer Finance |
| Zdroj: | Communications for Statistical Applications and Methods. 26:497-506 |
| ISSN: | 2383-4757 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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