Macroeconomic forecasting with echo state networks
Forecasting macroeconomic indicators plays a crucial role in economic planning and policy formulation. With the increasing availability of large datasets, there has been a surge in interest towards employing sophisticated forecasting models. This paper explores the performance of Echo State Networ...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175640 |
| _version_ | 1826116246195666944 |
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| author | Zhou, Qinghe |
| author2 | Juan-Pablo Ortega Lahuerta |
| author_facet | Juan-Pablo Ortega Lahuerta Zhou, Qinghe |
| author_sort | Zhou, Qinghe |
| collection | NTU |
| description | Forecasting macroeconomic indicators plays a crucial role in economic planning and policy formulation.
With the increasing availability of large datasets, there has been a surge in interest towards employing
sophisticated forecasting models. This paper explores the performance of Echo State Networks (ESN)
in forecasting Gross Domestic Product (GDP) growth, both one-period ahead and multi-step ahead. In
addition to ESN, traditional models such as Autoregressive model with lag 1 and Vector Autoregressive
models are included for comparison. The Model Confidence Set procedure is adopted to assess the
forecasting performance across these models. Through empirical analysis using US Macroeconomic
data, the study reveals that ESN exhibits notable forecasting performance, demonstrating its potential
as a valuable tool in macroeconomic forecasting. |
| first_indexed | 2024-10-01T04:08:12Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175640 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:08:12Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1756402024-05-06T15:36:55Z Macroeconomic forecasting with echo state networks Zhou, Qinghe Juan-Pablo Ortega Lahuerta School of Physical and Mathematical Sciences Lyudmila Grigoryeva juan-pablo.ortega@ntu.edu.sg, lyudmila.grigoryeva@unisg.ch Mathematical Sciences Social Sciences Echo state network GDP forecasting Forecasting macroeconomic indicators plays a crucial role in economic planning and policy formulation. With the increasing availability of large datasets, there has been a surge in interest towards employing sophisticated forecasting models. This paper explores the performance of Echo State Networks (ESN) in forecasting Gross Domestic Product (GDP) growth, both one-period ahead and multi-step ahead. In addition to ESN, traditional models such as Autoregressive model with lag 1 and Vector Autoregressive models are included for comparison. The Model Confidence Set procedure is adopted to assess the forecasting performance across these models. Through empirical analysis using US Macroeconomic data, the study reveals that ESN exhibits notable forecasting performance, demonstrating its potential as a valuable tool in macroeconomic forecasting. Bachelor's degree 2024-05-03T05:12:57Z 2024-05-03T05:12:57Z 2024 Final Year Project (FYP) Zhou, Q. (2024). Macroeconomic forecasting with echo state networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175640 https://hdl.handle.net/10356/175640 en application/pdf Nanyang Technological University |
| spellingShingle | Mathematical Sciences Social Sciences Echo state network GDP forecasting Zhou, Qinghe Macroeconomic forecasting with echo state networks |
| title | Macroeconomic forecasting with echo state networks |
| title_full | Macroeconomic forecasting with echo state networks |
| title_fullStr | Macroeconomic forecasting with echo state networks |
| title_full_unstemmed | Macroeconomic forecasting with echo state networks |
| title_short | Macroeconomic forecasting with echo state networks |
| title_sort | macroeconomic forecasting with echo state networks |
| topic | Mathematical Sciences Social Sciences Echo state network GDP forecasting |
| url | https://hdl.handle.net/10356/175640 |
| work_keys_str_mv | AT zhouqinghe macroeconomicforecastingwithechostatenetworks |