Applying Machine Learning Algorithms to Predict the Size of the Informal Economy
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- João Felix & Michel Alexandre & Gilberto Tadeu Lima, 2025. "Applying Machine Learning Algorithms to Predict the Size of the Informal Economy," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1169-1189, March.
References listed on IDEAS
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Cited by:
- Teddy Lazebnik, 2025. "Going a Step Deeper Down the Rabbit Hole: Deep Learning Model to Measure the Size of the Unregistered Economy Activity," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1759-1774, March.
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More about this item
Keywords
: Informal economy; machine learning; linear models; Shapley values;All these keywords.
JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2023-09-25 (Artificial Intelligence)
- NEP-BIG-2023-09-25 (Big Data)
- NEP-CMP-2023-09-25 (Computational Economics)
- NEP-GTH-2023-09-25 (Game Theory)
- NEP-IUE-2023-09-25 (Informal and Underground Economics)
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