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Niels Stender

Personal Details

First Name:Niels
Middle Name:
Last Name:Stender
Suffix:
RePEc Short-ID:pst199
http://www.innohead.com

Affiliation

School of Economics and Management
Institut for Økonomi
Aarhus Universitet

Aarhus, Denmark
http://www.econ.au.dk/
RePEc:edi:anaaudk (more details at EDIRC)

Research output

as
Jump to: Working papers

Working papers

  1. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.

Citations

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Working papers

  1. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.

    Cited by:

    1. Al Kez, Dlzar & Foley, Aoife & Abdul, Zrar Khald & Del Rio, Dylan Furszyfer, 2024. "Energy poverty prediction in the United Kingdom: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).
    2. Gianni Betti & Ruzhdie Bici & Laura Neri & Thomas Pave Sohnesen & Ledia Thomo, 2017. "Local Poverty and Inequality in Albania," Department of Economics University of Siena 745, Department of Economics, University of Siena.
    3. Baez, Javier E. & Kshirsagar, Varun & Skoufias, Emmanuel, 2024. "Drought-sensitive targeting and child growth faltering in Southern Africa," World Development, Elsevier, vol. 182(C).
    4. Walter Sosa-Escudero & Maria Victoria Anauati & Wendy Brau, 2022. "Poverty, Inequality and Development Studies with Machine Learning," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 291-335, Springer.
    5. Arranhado, Esmeralda & Barbosa, Lágida & Bastos, João A., 2025. "Multidimensional poverty in Benin," Socio-Economic Planning Sciences, Elsevier, vol. 99(C).
    6. Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
    7. Echevin, Damien & Fotso, Guy & Bouroubi, Yacine & Coulombe, Harold & Li, Qing, 2025. "Combining survey and census data for improved poverty prediction using semi-supervised deep learning," Journal of Development Economics, Elsevier, vol. 172(C).
    8. Thomas Pave Sohnesen & Peter Fisker & David Malmgren‐Hansen, 2022. "Using Satellite Data to Guide Urban Poverty Reduction," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 282-294, December.
    9. Peter Fisker & Jordi Gallego-Ayala & David Malmgren Hansen & Thomas Pave Sohnesen & Edmundo Murrugarra, 2022. "Guiding Social Protection Targeting Through Satellite Data in São Tomé and Príncipe," World Bank Publications - Reports 38222, The World Bank Group.
    10. Emmanuel A. Onsay & Jason Alinsunurin & Jomar F. Rabajante, 2025. "Optimizing machine learning algorithms for multidimensional poverty prediction in the Philippines," SN Business & Economics, Springer, vol. 5(10), pages 1-40, October.
    11. Beltramo, Theresa P. & Calvi, Rossella & De Giorgi, Giacomo & Sarr, Ibrahima, 2023. "Child poverty among refugees," World Development, Elsevier, vol. 171(C).

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (1) 2016-04-23

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