arXiv 6 Feb 2024 · Econometrics · publishedApuntes Revista de Ciencias Sociales (2025) · 3 citations (OpenAlex)
arXiv:2402.04165 · PDF · DOI · OpenAlex · Extracted main text
In the dynamic landscape of continuous change, Machine Learning (ML) "nowcasting" models offer a distinct advantage for informed decision-making in both public and private sectors. This study introduces ML-based GDP growth projection models for monthly rates in Peru, integrating structured macroeconomic indicators with high-frequency unstructured sentiment variables. Analyzing data from January 2007 to May 2023, encompassing 91 leading economic indicators, the study evaluates six ML algorithms to identify optimal predictors. Findings highlight the superior predictive capability of ML models using unstructured data, particularly Gradient Boosting Machine, LASSO, and Elastic Net, exhibiting a 20% to 25% reduction in prediction errors compared to traditional AR and Dynamic Factor Models (DFM). This enhanced performance is attributed to better handling of data of ML models in high-uncertainty periods, such as economic crises.
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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 | A. Richardson, T. Mulder, Nowcasting new zealand gdp using machine l… (2018) | 1.000 | 6 | 3 | 100% |
| 2 | H. Varian, Machine learning and econometrics, Slides package from ta… (2014) | 0.811 | 4 | 2 | 100% |
| 3 | Q. Zhang, H. Ni, H. Xu, Nowcasting chinese gdp in a data-rich enviro… (2023) 106204 | 0.811 | 4 | 2 | 100% |
| 4 | F. X. Diebold, R. S. Mariano, Comparing predictive accuracy, Journal… (1995) 253–263 | 0.737 | 3 | 2 | 100% |
| 5 | M. Bańbura, M. Modugno, Maximum likelihood estimation of factor mode… (2014) 133–160 | 0.644 | 2 | 2 | 100% |
| 6 | C. Romer, D. Romer, The fomc versus the staff: where can monetary po… (2008) 230–235 | 0.644 | 2 | 2 | 100% |
| 7 | N. Woloszko, A weekly tracker of activity based on machine learning… (2020) | 0.644 | 2 | 2 | 100% |
| 8 | H. Zou, T. Hastie, Regularization and variable selection via the ela… (2005) 301–320 | 0.644 | 2 | 2 | 100% |
| 9 | S. Athey, The impact of machine learning on economics, in: The econo… (2018) pp | 0.511 | 2 | 1 | 100% |
| 10 | J. J. Barrios, J. Escobar, J. Leslie, L. Martin, W. Peña, Nowcasting… (2021) | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 52 scored citations.