Colombian economic activity nowcasting: Addressing nonlinearities and high dimensionality through machine-learning
Economic decisions are made with high uncertainty about the current and recent past economic activity, due to the limited and imperfect available information. Therefore the following question arises: how can the accuracy of Colombian economic activity nowcasting be enhanced compared to traditional f...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2025 |
| País: | Colombia |
| Institución: | Universidad de los Andes |
| Repositorio: | Séneca: repositorio Uniandes |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uniandes.edu.co:1992/75464 |
| Acceso en línea: | https://hdl.handle.net/1992/75464 |
| Access Level: | acceso abierto |
| Palabra clave: | Nowcasting Forecasting Economic activity Macroeconomics Machine learning Risk aversion Loss function Economía |
| Sumario: | Economic decisions are made with high uncertainty about the current and recent past economic activity, due to the limited and imperfect available information. Therefore the following question arises: how can the accuracy of Colombian economic activity nowcasting be enhanced compared to traditional forecasting methods? This paper demonstrates: (a) using a risk-averse customized loss function that accounts for the agent disutility and penalizes directional discrepancies provides a useful alternative for assessing model performance by ensuring more accurate nowcasts, maximizing both precision and economic relevance. And (b) during periods of abrupt shocks and high volatility, such as the COVID-19 (2020–2021) and the post COVID-19 subsequent years (2022-2023), machine learning models outperform traditional nowcasting models. |
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