Applying Machine Learning Techniques to Estimate the Size of the Romanian Shadow Economy
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References
- Asllani, A., Dell’Anno, R., & Schneider, F. (2024). Mapping the informal economy worldwide with an enhanced MIMIC approach: New estimates for 110 countries from 1997-2022 (No. 11416). CESifo Working Paper.
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
- Cagan, P. (1958). The demand for currency is relative to the total money supply. Journal of Political Economy, 66(4), pp. 303-328.
- Dell’Anno, R., Davidescu, A. (2019). Estimating shadow economy and tax evasion in Romania. A comparison by different estimation approaches. Economic Analysis and Policy, 63.
- Feige, E. L. (1990). Defining and estimating underground and informal economies: The new institutional economics approach. World Development, 18(7), pp. 989-1002.
- Felix, J., Alexandre, M., & Lima, G. T. (2024). Applying machine learning algorithms to predict the size of the informal economy. Computational Economics, 1-21.
- Freund, C., Spatafora, N. (2008). Remittances, transaction costs, and informality. Journal of Development Economics, 86(2), pp. 356-366.
- Friedman, M. (1989). ‘Quantity theory of money’. In: Money. London: Palgrave Macmillan UK, pp. 1-40.
- Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.
- Géron, A. (2019). Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts. Google Kitaplar.
- Ivașcu, C., Ștefoni, S. E. (2023). Modelling the Nonlinear Dependencies between Government Expenditures and Shadow Economy Using Data-Driven Approaches. Scientific Annals of Economics and Business, 70(1), pp. 97–114.
- Koloane, C.T., Bodhlyera, O. (2022). A statistical approach to modelling the underground economy in South Africa. Journal of Economics and Management, 44, pp. 64-95.
- Moraru, C., Popovici, N. (2014). Analysis of Fiscal Policy Measures during 2005 - 2013. The Case of Romania. Ovidius University Annals, Economic Sciences Series, 0(2), pp. 224-228.
- Schneider, F., Enste, D. H. (2000). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, 38(1), pp. 77–114.
- Schneider, F., Buehn, A., & Montenegro, C. E. (2010). New estimates for the shadow economies all over the world. International Economic Journal, 24(4), 443-461.
- Schneider, F. (2013). The shadow economy in Europe, 2013. A.T. Kearney & Visa Europe.
- Shami, L., & Lazebnik, T. (2024). Implementing machine learning methods in estimating the size of the non-observed economy. Computational Economics, 63(4), 1459-1476.
- Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222.
- Tanzi, V. (1980). The Underground Economy in the United States: Estimates and Implications. Banca Nazionale del Lavoro, 135(4), pp. 427-453.
- Tanzi, V. (1983). The Underground Economy in the United States: Annual Estimates, 1930-1980. IMF-Staff Papers, 30(2), pp. 283-305.
DOI: https://doi.org/10.2478/picbe-2025-0196 | Journal eISSN: 2558-9652
Language: English
Page range: 2525 - 2541
Published on: Jul 24, 2025
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
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© 2025 Andreea-Daniela Ivan, Adriana Anamaria Davidescu, Marina-Diana Agafiţei, Maria Cristina Geambaşu, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.