Title: Nowcasting US GDP with artificial neural networks
Abstract: We use a machine learning approach to forecast the US GDP value of the current quarter and several quarters ahead. Within each quarter, the contemporaneous value of GDP growth is unavailable but can be estimated using higher-frequency variables that are published in a more timely manner. Using the monthly FRED-MD database, we compare the feedforward artificial neural network forecasts of GDP growth to forecasts of state of the art dynamic factor models and the Survey of Professional Forecasters, and we evaluate the relative performance. The results indicate that the neural network outperforms the dynamic factor model in terms of now- and forecasting, while it generates at least as good now- and forecasts as the Survey of Professional Forecasters.
Publication Year: 2019
Publication Date: 2019-01-01
Language: en
Type: article
Access and Citation
Cited By Count: 5
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