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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.00249 |
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| _version_ | 1866912232485421056 |
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| author | Sengupta, Shovon Chakraborty, Tanujit Singh, Sunny Kumar |
| author_facet | Sengupta, Shovon Chakraborty, Tanujit Singh, Sunny Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00249 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Forecasting CPI inflation under economic policy and geopolitical uncertainties Sengupta, Shovon Chakraborty, Tanujit Singh, Sunny Kumar Econometrics 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. |
| title | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| topic | Econometrics |
| url | https://arxiv.org/abs/2401.00249 |