Shovon Sengupta, Tanujit Chakraborty, Sunny Kumar Singh
arXiv 30 Dec 2023 · Econometrics · publishedInternational Journal of Forecasting (2024) · 17 citations (OpenAlex)
arXiv:2401.00249 · PDF · DOI · OpenAlex · Extracted main text
Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at the central banks. This study introduces a filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, which is tested on BRIC countries. FEWNet breaks down inflation data into high and low-frequency components using wavelets and utilizes them along with other economic factors (economic policy uncertainty and geopolitical risk) to produce forecasts. All the wavelet-transformed series and filtered exogenous variables are fed into downstream autoregressive neural networks to make the final ensemble forecast. Theoretically, we show that FEWNet reduces the empirical risk compared to fully connected autoregressive neural networks. FEWNet is more accurate than other forecasting methods and can also estimate the uncertainty in its predictions due to its capacity to effectively capture non-linearities and long-range dependencies in the data through its adaptable architecture. This makes FEWNet a valuable tool for central banks to manage inflation.
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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 | Hyndman, R. J., & Athanasopoulos, G (2018) Forecasting: principles and practice | 1.000 | 5 | 3 | 100% |
| 2 | Panja, M., Chakraborty, T., Kumar, U., & Liu, N (2023) Epicasting: An ensemble wavelet neural network for forecasting epidemics self | 0.928 | 4 | 3 | 100% |
| 3 | Percival, D. B., & Walden, A. T (2000) Wavelet methods for time series analysis | 0.830 | 7 | 4 | 57% |
| 4 | Baker, S. R., Bloom, N., & Davis, S. J (2016) Measuring economic policy uncertainty | 0.811 | 4 | 2 | 100% |
| 5 | Balcilar, M., Gupta, R., & Jooste, C (2017) Long memory, economic policy uncertainty and forecasting us inflation: a bayesian varfima approach | 0.811 | 4 | 2 | 100% |
| 6 | Caldara, D., & Iacoviello, M (2022) Measuring geopolitical risk | 0.811 | 4 | 2 | 100% |
| 7 | Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., & Zilberman, E (2021) Forecasting inflation in a data-rich environment: the benefits of machine learning methods | 0.737 | 3 | 2 | 100% |
| 8 | Percival, D. B., & Mofjeld, H. O (1997) Analysis of subtidal coastal sea level fluctuations using wavelets | 0.644 | 4 | 2 | 50% |
| 9 | Adeosun, O. A., Tabash, M. I., Vo, X. V., & Anagreh, S (2023) Uncertainty measures and inflation dynamics in selected global players: a wavelet approach | 0.644 | 2 | 2 | 100% |
| 10 | Aminghafari, M., & Poggi, J.-M (2007) Forecasting time series using wavelets | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 97 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks | 0.644 | 2 | 2 |
| 2 | Neural ARFIMA model for forecasting BRIC exchange rates with long memory under oil shocks and policy uncertainties | 0.405 | 1 | 1 |