Artikel
Nonlinear inflation forecasting with recurrent neural networks
Motivated by the recent literature that finds that artificial neural networks (NN) can efficiently predict economic time‐series in general and inflation in particular, we investigate if the forecasting performance can be improved even further by using a particular kind of NN—a recurrent neural network. We use a long short‐term memory recurrent neural network (LSTM) that was proven to be highly efficient for sequential data and computed univariate forecasts of monthly US CPI inflation. We show that even though LSTM slightly outperforms autoregressive model (AR), NN, and Markov‐switching models, its performance is on par with the seasonal autoregressive model SARIMA. Additionally, we conduct a sensitivity analysis with respect to hyperparameters and provide a qualitative interpretation of what the networks learn by applying a novel layer‐wise relevance propagation technique.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Forecasting ; ISSN: 1099-131X ; Volume: 42 ; Year: 2022 ; Issue: 2 ; Pages: 240-259 ; Hoboken, NJ: Wiley
- Subject
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forecasting
inflation
LRP
LSTM
neural networks
SARIMA
- Event
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Geistige Schöpfung
- (who)
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Almosova, Anna
Andresen, Niek
- Event
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Veröffentlichung
- (who)
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Wiley
- (where)
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Hoboken, NJ
- (when)
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2022
- DOI
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doi:10.1002/for.2901
- Last update
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10.03.2025, 11:45 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Almosova, Anna
- Andresen, Niek
- Wiley
Time of origin
- 2022
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