IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v63y2024i4d10.1007_s10614-023-10369-4.html
   My bibliography  Save this article

Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy

Author

Listed:
  • Labib Shami

    (Western Galilee College)

  • Teddy Lazebnik

    (University College London)

Abstract

Even though the literature on unregistered economic activity is growing at an increasing rate, we commonly encounter simple ordinary least squares methods and panel regressions, largely ignoring the recent rapid developments in machine learning methods. This study provides a new approach to more accurately estimate the size of the non-observed economy using machine learning methods. Compared to two currency demand-based models used to estimate the size of the non-observed economy, we show that a Random Forest algorithm can more accurately estimate the demand for currency, which is known to provide a fair estimation of the unregistered economic activity. The proposed approach shows superior forecasting capabilities compared to the current state-of-the-art linear regression-based methods dedicated to estimating non-observed economic activity.

Suggested Citation

  • Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
  • Handle: RePEc:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10369-4
    DOI: 10.1007/s10614-023-10369-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10369-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10614-023-10369-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feige, Edgar L., 2015. "Reflections on the meaning and measurement of Unobserved Economies: What do we really know about the “Shadow Economy”?," MPRA Paper 68466, University Library of Munich, Germany.
    2. Lars P. Feld & Friedrich Schneider, 2010. "Survey on the Shadow Economy and Undeclared Earnings in OECD Countries," German Economic Review, Verein für Socialpolitik, vol. 11(2), pages 109-149, May.
    3. Ha, Le Thanh & Dung, Hoang Phuong & Thanh, To Trung, 2021. "Economic complexity and shadow economy: A multi-dimensional analysis," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 408-422.
    4. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    5. Piotr Dybka & Michał Kowalczuk & Bartosz Olesiński & Andrzej Torój & Marek Rozkrut, 2019. "Currency demand and MIMIC models: towards a structured hybrid method of measuring the shadow economy," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(1), pages 4-40, February.
    6. repec:bla:germec:v:11:y:2010:i::p:109-149 is not listed on IDEAS
    7. Kenneth Rogoff, 2015. "Costs and Benefits to Phasing out Paper Currency," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 445-456.
    8. Friedrich Schneider & Andreas Buehn & Claudio Montenegro, 2010. "New Estimates for the Shadow Economies all over the World," International Economic Journal, Taylor & Francis Journals, vol. 24(4), pages 443-461.
    9. Friedrich SCHNEIDER, 2016. "Estimating the Size of the Shadow Economy: Methods, Problems and Open Questions," Turkish Economic Review, KSP Journals, vol. 3(2), pages 256-280, June.
    10. Kerem Cantekin & Ceyhun Elgin, 2017. "Extent And Growth Effects Of Informality In Turkey: Evidence From A Firm-Level Survey," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 62(05), pages 1017-1037, December.
    11. Dominik H. Enste & Friedrich Schneider, 2000. "Shadow Economies: Size, Causes, and Consequences," Journal of Economic Literature, American Economic Association, vol. 38(1), pages 77-114, March.
    12. Ceyhun Elgin & Oguz Oztunali, 2012. "Shadow Economies around the World: Model Based Estimates," Working Papers 2012/05, Bogazici University, Department of Economics.
    13. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    14. Weber, Gerhard-Wilhelm & Defterli, Ozlem & Alparslan Gök, SIrma Zeynep & Kropat, Erik, 2011. "Modeling, inference and optimization of regulatory networks based on time series data," European Journal of Operational Research, Elsevier, vol. 211(1), pages 1-14, May.
    15. Friedrich Schneider & Dominik Enste, 1999. "Shadow Economies Around the World - Size, Causes, and Consequences," CESifo Working Paper Series 196, CESifo.
    16. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    17. Lars P. Feld & Claus Larsen, 2012. "Undeclared Work, Deterrence and Social Norms," Springer Books, Springer, edition 127, number 978-3-540-87401-0, July.
    18. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    19. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    20. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    21. Guerino Ardizzi & Carmelo Petraglia & Massimiliano Piacenza & Gilberto Turati, 2014. "Measuring the Underground Economy with the Currency Demand Approach: A Reinterpretation of the Methodology, With an Application to Italy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 747-772, December.
    22. Ceyhun Elgin & Ferda Erturk, 2019. "Informal economies around the world: measures, determinants and consequences," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 9(2), pages 221-237, June.
    23. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    24. Shami, Labib, 2019. "Dynamic monetary equilibrium with a Non-Observed Economy and Shapley and Shubik’s price mechanism," Journal of Macroeconomics, Elsevier, vol. 62(C).
    25. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    26. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    27. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    28. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    29. Trevor Breusch, 2005. "Estimating the Underground Economy using MIMIC Models," Econometrics 0507003, University Library of Munich, Germany, revised 15 Dec 2005.
    30. J. Ferwerda & I. Deleanu & B. Unger, 2010. "Revaluating the Tanzi-Model to Estimate the Underground Economy," Working Papers 10-04, Utrecht School of Economics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.
    2. Kose, M. Ayhan & Elgin, Ceyhun & Ohnsorge, Franziska & Yu, Shu, 2021. "Understanding Informality," CEPR Discussion Papers 16497, C.E.P.R. Discussion Papers.
    3. Ceyhun Elgin & M. ayhan Köse & Franziska Ohnsorge & Shu Yu, 2021. "Understanding Informality Abstract:," Working Papers 2021/03, Bogazici University, Department of Economics.
    4. Friedrich Schneider & Mangirdas Morkunas & Erika Quendler, 2023. "An estimation of the informal economy in the agricultural sector in the EU‐15 from 1996 to 2019," Agribusiness, John Wiley & Sons, Ltd., vol. 39(2), pages 406-447, March.
    5. Owolabi, Adegboyega O. & Berdiev, Aziz N. & Saunoris, James W., 2022. "Is the shadow economy procyclical or countercyclical over the business cycle? International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 257-270.
    6. Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
    7. Miguel Ángel Echarte Fernández & Sergio Luis Náñez Alonso & Ricardo Reier Forradellas & Javier Jorge-Vázquez, 2022. "From the Great Recession to the COVID-19 Pandemic: The Risk of Expansionary Monetary Policies," Risks, MDPI, vol. 10(2), pages 1-17, January.
    8. K K C Sineth Kannangara & Yanrui Wu, 2023. "Shadow Economy in Sri Lanka: A Review and New Estimates," Economics Discussion / Working Papers 23-04, The University of Western Australia, Department of Economics.
    9. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    10. Donal Mac Géidigh & Friedrich Schneider & Matthias Blum, 2016. "Grey Matters: Charting the Development of the Shadow Economy," CESifo Working Paper Series 6234, CESifo.
    11. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm a," Energy, Elsevier, vol. 288(C).
    12. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    13. Philippe Adair, 2017. "Non-Observed Economy vs. the Shadow Economy in the EU: The Accuracy of Measurements Methods and Estimates revisited," Post-Print hal-01683929, HAL.
    14. Oliver Hümbelin & Lukas Hobi & Robert Fluder, 2021. "Rich Cities, Poor Countryside? Social Structure of the Poor and Poverty Risks in Urban and Rural Places in an Affluent Country. An Administrative Data based Analysis using Random Forest," University of Bern Social Sciences Working Papers 40, University of Bern, Department of Social Sciences, revised 10 Nov 2021.
    15. Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.
    16. Schneider, Friedrich, 2017. "Restricting or Abolishing Cash: An Effective Instrument for Fighting the Shadow Economy, Crime and Terrorism?," International Cash Conference 2017 – War on Cash: Is there a Future for Cash? 162914, Deutsche Bundesbank.
    17. Saeed Nosratabadi & Nesrine Khazami & Marwa Ben Abdallah & Zoltan Lackner & Shahab S. Band & Amir Mosavi & Csaba Mako, 2020. "Social Capital Contributions to Food Security: A Comprehensive Literature Review," Papers 2012.03606, arXiv.org.
    18. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    19. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
    20. Di Wu & Zhenning Xu & Seung Bach, 2023. "Using Google Trends to predict and forecast avocado sales," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 629-641, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:63:y:2024:i:4:d:10.1007_s10614-023-10369-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.