Have a personal or library account? Click to login
Applying Machine Learning Techniques to Estimate the Size of the Romanian Shadow Economy Cover

Applying Machine Learning Techniques to Estimate the Size of the Romanian Shadow Economy

Open Access
|Jul 2025

References

  1. 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.
  2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
  3. Cagan, P. (1958). The demand for currency is relative to the total money supply. Journal of Political Economy, 66(4), pp. 303-328.
  4. 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.
  5. Feige, E. L. (1990). Defining and estimating underground and informal economies: The new institutional economics approach. World Development, 18(7), pp. 989-1002.
  6. Felix, J., Alexandre, M., & Lima, G. T. (2024). Applying machine learning algorithms to predict the size of the informal economy. Computational Economics, 1-21.
  7. Freund, C., Spatafora, N. (2008). Remittances, transaction costs, and informality. Journal of Development Economics, 86(2), pp. 356-366.
  8. Friedman, M. (1989). ‘Quantity theory of money’. In: Money. London: Palgrave Macmillan UK, pp. 1-40.
  9. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.
  10. Géron, A. (2019). Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts. Google Kitaplar.
  11. 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.
  12. 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.
  13. 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.
  14. Schneider, F., Enste, D. H. (2000). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, 38(1), pp. 77–114.
  15. 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.
  16. Schneider, F. (2013). The shadow economy in Europe, 2013. A.T. Kearney & Visa Europe.
  17. Shami, L., & Lazebnik, T. (2024). Implementing machine learning methods in estimating the size of the non-observed economy. Computational Economics, 63(4), 1459-1476.
  18. Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222.
  19. Tanzi, V. (1980). The Underground Economy in the United States: Estimates and Implications. Banca Nazionale del Lavoro, 135(4), pp. 427-453.
  20. Tanzi, V. (1983). The Underground Economy in the United States: Annual Estimates, 1930-1980. IMF-Staff Papers, 30(2), pp. 283-305.
Language: English
Page range: 2525 - 2541
Published on: Jul 24, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 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.