Forecasting CPI inflation under economic policy and geopolitical uncertainties
Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into hig...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1675 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1857415064401739776 |
|---|---|
| author | Sengupta, Shovon |
| author2 | Chakraborty, Tanujit Singh, Sunny Kumar |
| author2_role | author author |
| author_facet | Sengupta, Shovon Chakraborty, Tanujit Singh, Sunny Kumar |
| author_role | author |
| dc.creator.none.fl_str_mv | Sengupta, Shovon Chakraborty, Tanujit Singh, Sunny Kumar |
| dc.date.none.fl_str_mv | 2024-09-20T05:29:54Z 2024-09-20T05:29:54Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1675 10.1016/j.ijforecast.2024.08.005 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | International Journal of Forecasting |
| dc.subject.none.fl_str_mv | Inflation forecasting Wavelets Neural networks Empirical risk minimization Conformal prediction intervals |
| dc.title.none.fl_str_mv | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into high- and low-frequency components using wavelet transforms, and incorporates additional economic factors, such as economic policy uncertainty and geopolitical risk, to enhance forecast accuracy. These wavelet-transformed series and filtered exogenous variables are input into downstream autoregressive neural networks, producing the final ensemble forecast. Theoretically, we demonstrate that FEWNet reduces empirical risk compared to fully connected autoregressive neural networks. Empirically, FEWNet outperforms other forecasting methods and effectively estimates prediction uncertainty, due to its ability to capture non-linearities and long-range dependencies through its adaptable architecture. Consequently, FEWNet emerges as a valuable tool for central banks to manage inflation and enhance monetary policy decisions. |
| id | sorbonner_4e39f31c6972688d30afb5ca7bf9f7da |
| identifier_str_mv | 10.1016/j.ijforecast.2024.08.005 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1675 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Forecasting CPI inflation under economic policy and geopolitical uncertaintiesSengupta, ShovonChakraborty, TanujitSingh, Sunny KumarInflation forecastingWaveletsNeural networksEmpirical risk minimizationConformal prediction intervalsForecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into high- and low-frequency components using wavelet transforms, and incorporates additional economic factors, such as economic policy uncertainty and geopolitical risk, to enhance forecast accuracy. These wavelet-transformed series and filtered exogenous variables are input into downstream autoregressive neural networks, producing the final ensemble forecast. Theoretically, we demonstrate that FEWNet reduces empirical risk compared to fully connected autoregressive neural networks. Empirically, FEWNet outperforms other forecasting methods and effectively estimates prediction uncertainty, due to its ability to capture non-linearities and long-range dependencies through its adaptable architecture. Consequently, FEWNet emerges as a valuable tool for central banks to manage inflation and enhance monetary policy decisions.2024-09-20T05:29:54Z2024-09-20T05:29:54Z2024Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdfhttps://depot.sorbonne.ae/handle/20.500.12458/167510.1016/j.ijforecast.2024.08.005enInternational Journal of Forecastingoai:depot.sorbonne.ae:20.500.12458/16752024-09-20T18:00:28Z |
| spellingShingle | Forecasting CPI inflation under economic policy and geopolitical uncertainties Sengupta, Shovon Inflation forecasting Wavelets Neural networks Empirical risk minimization Conformal prediction intervals |
| title | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| title_full | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| title_fullStr | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| title_full_unstemmed | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| title_short | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| title_sort | Forecasting CPI inflation under economic policy and geopolitical uncertainties |
| topic | Inflation forecasting Wavelets Neural networks Empirical risk minimization Conformal prediction intervals |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1675 |