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Data Science for economics and social science

Statistical learning; analysis and methods for big & complex data; data mining; patterns of causality in economic data; statistical model checking; calibration and validation of economic models
Data Science

The faculty members of this area use and develop statistical learning techniques and methods to study big and complex data, perform data mining, investigate the causality patterns in economic data, calibrate and validate economic models. The research in this area revolves around the activities of the EMbeDS Department of Excellence coordinated by Prof. Francesca Chiaromonte.

Ricerca Economia - grafica per data science


Major Publications

  1. Boschi T., Di Iorio J., Testa L., Cremona M., Chiaromonte F. (2021), “Functional Data Analysis characterizes the shapes of the first COVID-19 epidemic wave in Italy”. Scientific Reports 11, 17054.
  2. Fontana M., Iori M., Montobbio F., Sinatra R. (2020), “New and atypical combinations: An assessment of novelty and interdisciplinarity”, Research Policy 49 (7); DOI: 10.1016/j.respol.2020.104063
  3. Magazzini L., Calzolari G. (2020), “Testing Initial Conditions in Dynamic Panel Data Models”, Econometric Reviews, 39(2), 115-134
  4. Coronese M., Lamperti F., Keller K., Chiaromonte F. and Roventini A. (2019), “Evidence for sharp increase in the economic damages of extreme natural disasters”, Proceedings of the National Academy of Sciences USA. 116 (43) 21450-21455.
  5. Lamperti F, Roventini A., Sani A. (2018), “Agent-based model calibration using machine learning surrogates”, Journal of Economic Dynamics & Control, 90, pp. 366-389
  6. Cardelli L., M. Tribastone, M. Tschaikowski, A. Vandin. (2017), “Maximal aggregation of polynomial dynamical systems”. Proceedings of the National Academy of Sciences USA 114(38): 10029-10034
  7. Romano M.F., Sardella M.V., Alboni F. (2016), “Web Health Monitoring Survey: a new approach to enhance effectiveness of telemedicine systems”. JMIR Research Protocols, vol. 5, ISSN: 1929-0748, doi: 10.2196/resprot.5187
  8. Bottazzi G., D. Pirino and F. Tamagni (2015), "Zipf Law and the Firm Size Distribution: a critical discussion of popular estimators", Journal of Evolutionary Economics, 25(3), 585-610
  9. Moneta, A., Entner, D., Hoyer, P. O., & Coad, A. (2013), “Causal inference by independent component analysis: Theory and applications”. Oxford Bulletin of Economics and Statistics, 75(5), 705-730.
  10. Li B., Zha H. and Chiaromonte F. (2005), “Contour regression: a general approach to dimension reduction”. Annals of Statistics, 33(4), 1580-1616.