Nowcasting US GDP with artificial neural networks

Julius Loermann and Benedikt Maas

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Nowcasting; Machine learning; Neural networks; Big data (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 E32 (search for similar items in EconPapers)
Date: 2019-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac, nep-ore and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:95459

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