Aurelia Valiño Castro
(Aurelia Valino Castro)

Personal Details

First Name:	Aurelia
Middle Name:
Last Name:	Valino Castro
Suffix:
RePEc Short-ID:	pva161
	[This author has chosen not to make the email address public]
Terminal Degree:	Departamento de Economía Aplicada, Pública y Política; Facultad de Ciencias Económicas y Empresariales; Universidad Complutense de Madrid (from RePEc Genealogy)

Affiliation

Facultad de Ciencias Económicas y Empresariales
Universidad Complutense de Madrid

Madrid, Spain
https://economicasyempresariales.ucm.es/
RePEc:edi:feucmes (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

Sorry, no citations of working papers recorded.

Articles

1. Mongan, Juan Carlos & Santin, Daniel & Valiño, Aurelia, 2011. "Towards the equality of educational opportunity in the province of Buenos Aires," Journal of Policy Modeling, Elsevier, vol. 33(4), pages 583-596, July.

   Cited by:

   1. Rajius Idzalika & Maria C. Lo Bue, 2016. "Opportunities in education: are factors outside individual responsibility really persistent? Evidence from Indonesia, 1997-2007," Working Papers 397, ECINEQ, Society for the Study of Economic Inequality.
   2. Ponzo, Michela, 2013. "Does bullying reduce educational achievement? An evaluation using matching estimators," Journal of Policy Modeling, Elsevier, vol. 35(6), pages 1057-1078.
      • Ponzo, Michela, 2012. "Does bullying reduce educational achievement? An evaluation using matching estimators," MPRA Paper 36064, University Library of Munich, Germany.
   3. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
      • Kristof de Witte & Laura López-Torres, 2015. "Efficiency in Education. A Review of Literature and a Way Forward," Working Papers 1501, Departament Empresa, Universitat Autònoma de Barcelona, revised Apr 2015.
   4. Bhatnagar, Abhishek & Bolia, Nomesh B., 2019. "Improved governance of Indian school system through school consolidation," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1160-1178.
   5. Groot, Loek & van der Linde, Daan & Vincent, Charlotte, 2018. "Inequality of opportunity in the United Kingdom, 1991–2008," Journal of Policy Modeling, Elsevier, vol. 40(6), pages 1255-1271.
   6. Clifford Afoakwah & Xin Deng & Ilke Onur, 2023. "Reforms and education inequality in Ghana," Review of Development Economics, Wiley Blackwell, vol. 27(2), pages 853-878, May.
   7. Michela Ponzo & Vincenzo Scoppa, 2011. "The Long-Lasting Effects Of School Entry Age: Evidence From Italian Students," Working Papers 201101, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
      • Ponzo, Michela & Scoppa, Vincenzo, 2014. "The long-lasting effects of school entry age: Evidence from Italian students," Journal of Policy Modeling, Elsevier, vol. 36(3), pages 578-599.
   8. Agasisti, Tommaso & Cordero-Ferrera, Jose M., 2013. "Educational disparities across regions: A multilevel analysis for Italy and Spain," Journal of Policy Modeling, Elsevier, vol. 35(6), pages 1079-1102.

2. Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.

   Cited by:

   1. Santin, Daniel, 2004. "On the Approximation of Production Functions: A Comparison of Artificial Neural Networks Frontiers and Efficiency Techniques," Efficiency Series Papers 2004/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
      • Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
   2. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
   3. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," AQR Working Papers 201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.
      • Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
   4. Alves, André Bernardo & Wanke, Peter & Antunes, Jorge & Chen, Zhongfei, 2020. "Endogenous network efficiency, macroeconomy, and competition: Evidence from the Portuguese banking industry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
   5. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
   6. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
   7. George Halkos & Mike G. Tsionas, 2019. "Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1005-1025, October.
   8. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.
   9. Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.
   10. Francisco J. Delgado, 2005. "Measuring efficiency with neural networks. An application to the public sector," Economics Bulletin, AccessEcon, vol. 3(15), pages 1-10.
   11. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
   12. Yong Tan & Peter Wanke & Jorge Antunes & Ali Emrouznejad, 2021. "Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis," Annals of Operations Research, Springer, vol. 306(1), pages 131-171, November.
   13. Michael Dietrich, 2005. "Using simple neural networks to analyse firm activity," Working Papers 2005014, The University of Sheffield, Department of Economics, revised Jul 2005.
   14. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
   15. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
   16. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2010. "Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks," European Journal of Operational Research, Elsevier, vol. 206(2), pages 479-487, October.
   17. Azadeh, A. & Ghaderi, S.F. & Anvari, M. & Saberi, M., 2007. "Performance assessment of electric power generations using an adaptive neural network algorithm," Energy Policy, Elsevier, vol. 35(6), pages 3155-3166, June.
   18. Shivi Agarwal, 2016. "DEA-neural networks approach to assess the performance of public transport sector of India," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 248-258, June.
   19. Jorge Antunes & Abdollah Hadi-Vencheh & Ali Jamshidi & Yong Tan & Peter Wanke, 2022. "Bank efficiency estimation in China: DEA-RENNA approach," Annals of Operations Research, Springer, vol. 315(2), pages 1373-1398, August.

3. Aurelia Valino Castro, 2001. "Defence spending in Spain," Defence and Peace Economics, Taylor & Francis Journals, vol. 12(5), pages 395-415.

   Cited by:

   1. Javier Garc�a-Est�vez & Elisa Trujillo-Baute, 2014. "Drivers of R&D investment in the defence industry: evidence from Spain," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(1), pages 39-49, February.

Books

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More information

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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 2 papers 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-REG: Regulation (2) 2016-11-13 2018-06-11
2. NEP-COM: Industrial Competition (1) 2016-11-13
3. NEP-ICT: Information and Communication Technologies (1) 2016-11-13
4. NEP-IND: Industrial Organization (1) 2016-11-13
5. NEP-MIC: Microeconomics (1) 2016-11-13
6. NEP-MKT: Marketing (1) 2016-11-13
7. NEP-PAY: Payment Systems and Financial Technology (1) 2016-11-13

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