arXiv 8 Apr 2021 · Econometrics · publishedInternational Journal of Forecasting (2025) · 9 citations (OpenAlex)
arXiv:2104.03757 · PDF · DOI · OpenAlex · Extracted main text
This paper applies a recurrent neural network, the LSTM, to forecast inflation. This is an appealing model for time series as it processes each time step sequentially and explicitly learns dynamic dependencies. The paper also explores the dimension reduction capability of the model to uncover economically-meaningful factors that can explain the inflation process. Results from an exercise with US data indicate that the estimated neural nets present competitive, but not outstanding, performance against common benchmarks (including other machine learning models). The LSTM in particular is found to perform well at long horizons and during periods of heightened macroeconomic uncertainty. Interestingly, LSTM-implied factors present high correlation with business cycle indicators, informing on the usefulness of such signals as inflation predictors. The paper also sheds light on the impact of network initialization and architecture on forecast performance.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McCracken, M. W., and S. Ng (2016) FRED-MD: A monthly database for Macroeconomic Research. Journal of Business & Economic Statistics 34, 4, 574–589 | 0.941 | 6 | 4 | 83% |
| 2 | Coulombe, P. G (2022) A neural Phillips curve and a deep output gap. Available at SSRN: https://ssrn.com/abstract=4018079 | 0.874 | 5 | 2 | 100% |
| 3 | Diebold, F., and R. Mariano (1995) Comparing Predictive Accuracy. Journal of Business & Economic Statistics 13, 3, 253–263 | 0.843 | 3 | 3 | 100% |
| 4 | Medeiros, M. C., G. Vasconcelos, A. Veiga, and E. Zilberman (2019) Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods. Journal of Business & Economic Stati… | 0.811 | 4 | 2 | 100% |
| 5 | Giacomini, R., and B. Rossi (2010) Forecast Comparisons in Unstable Environments. Journal of Applied Econometrics 25, 595–620 | 0.737 | 4 | 2 | 75% |
| 6 | Buckmann, M., and A. Joseph (2022) An interpretable machine learning workflow with an application to economic forecasting. Bank of England Working Paper No 984 | 0.737 | 3 | 2 | 100% |
| 7 | Stock, J. H., and M. W. Watson (2007) Why Has U.S | 0.644 | 3 | 2 | 67% |
| 8 | Stock, J. H., and M. W. Watson (2002) Macroeconomic forecasting with diffusion indexes. Journal of Business & Economic Statistics 20, 147–162 | 0.644 | 2 | 2 | 100% |
| 9 | Goodfellow, I. J., Y. Bengio, and A. Courville (2016) Deep Learning | 0.511 | 2 | 2 | 50% |
| 10 | Clark, T. E., and K. D. West (2007) Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics 138, 291–311 | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 56 scored citations.