Marcella Corduas

Personal Details

First Name:	Marcella
Middle Name:
Last Name:	Corduas
Suffix:
RePEc Short-ID:	pco802
	https://www.docenti.unina.it/MARCELLA.CORDUAS

Affiliation

Università degli Studi di Napoli Federico II, Dipartimento di Scienze Politiche (University of Naples, Department of Political Sciences)

http://scienzepolitiche.dip.unina.it/
Napoli (ITALY)

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

1. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2009. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fairtrade Coffee," 2009 International European Forum, February 15-20, 2009, Innsbruck-Igls, Austria 59209, International European Forum on System Dynamics and Innovation in Food Networks.
   • Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(1), pages 1-12.

   Cited by:

   1. Gaëlle BALINEAU, 2017. "Fair Trade? Yes, but not at Christmas! Evidence from scanner data on real French Fairtrade purchases," Working Paper ab9a0fd1-6ad5-441b-879b-3, Agence française de développement.
   2. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.
   3. Van Loo, Ellen J. & Caputo, Vincenzina & Nayga, Rodolfo M. & Seo, Han-Seok & Zhang, Baoyue & Verbeke, Wim, 2015. "Sustainability labels on coffee: Consumer preferences, willingness-to-pay and visual attention to attributes," Ecological Economics, Elsevier, vol. 118(C), pages 215-225.
   4. Volker Lingnau & Florian Fuchs & Florian Beham, 2019. "The impact of sustainability in coffee production on consumers’ willingness to pay–new evidence from the field of ethical consumption," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 30(1), pages 65-93, April.
   5. Corduas, Marcella, 2015. "A statistical model for consumer preferences: the case of Italian extra virgin olive oil," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202701, European Association of Agricultural Economists.
   6. Hellberg-Bahr, Anneke & Pfeuffer, Martin & Spiller, Achim & Brümmer, Bernhard, 2011. "Using Price Rigidities to Explain Pricing Strategies in the Organic Milk Chain," 2011 International European Forum, February 14-18, 2011, Innsbruck-Igls, Austria 122003, International European Forum on System Dynamics and Innovation in Food Networks.
   7. Arboretti Giancristofaro, Rosa & Bordignon, Paolo, 2015. "Consumer preferences in food packaging: cub models and conjoint analysis," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202707, European Association of Agricultural Economists.
   8. Fitzsimmons, Jill & Cicia, Gianni, 2018. "Different Tubers for Different Consumers: Heterogeneity in Human Values and Willingness to Pay for Social Outcomes of Potato Credence Attributes," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 9(4), August.
   9. Takahashi, R. & Todo, Y., 2018. "When do consumers stand up for the environment? Evidence from a large-scale social experiment to promote environmentally friendly coffee," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277507, International Association of Agricultural Economists.
   10. Takahashi, Ryo & Todo, Yasuyuki & Funaki, Yukihiko, 2018. "How Can We Motivate Consumers to Purchase Certified Forest Coffee? Evidence From a Laboratory Randomized Experiment Using Eye-trackers," Ecological Economics, Elsevier, vol. 150(C), pages 107-121.
   11. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
   12. Rotaris Lucia & Danielis Romeo, 2011. "Willingness to Pay for Fair Trade Coffee: A Conjoint Analysis Experiment with Italian Consumers," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 9(1), pages 1-22, June.
   13. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 303-321, December.
   14. Francesca Colantuoni & Gianni Cicia & Teresa Del Giudice & Daniel Lass & Francesco Caracciolo & Pasquale Lombardi, 2016. "Heterogeneous Preferences for Domestic Fresh Produce: Evidence from German and Italian Early Potato Markets," Agribusiness, John Wiley & Sons, Ltd., vol. 32(4), pages 512-530, November.
   15. Panico, Teresa & Verneau, Fabio & Capone, Vincenza & La Barbera, Francesco & Del Giudice, Teresa, 2017. "Antecedents of Intention and Behavior Towards Fair Trade Products: A Study on Values and Attitudes in Italy," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 8(2), March.

Articles

1. Piccolo, Domenico & Capecchi, Stefania & Iannario, Maria & Corduas, Marcella, 2013. "Modelling Consumer Preferences For Extra Virgin Olive Oil: The Italian Case," Politica Agricola Internazionale - International Agricultural Policy, Edizioni L'Informatore Agrario, vol. 2013(1), March.

   Cited by:

   1. Lucio Cappelli & Fabrizio D’Ascenzo & Maria Felice Arezzo & Roberto Ruggieri & Irina Gorelova, 2020. "The Willingness to Pay in the Food Sector. Testing the Hypothesis of Consumer Preferences for Some Made in Italy Products," Sustainability, MDPI, vol. 12(15), pages 1-11, August.
   2. Barbara Cafarelli & Piermichele La Sala & Giustina Pellegrini & Mariantonietta Fiore, 2017. "Consumers? preferences investigation for extra virgin olive oil basing on conjoint analysis," RIVISTA DI STUDI SULLA SOSTENIBILITA', FrancoAngeli Editore, vol. 0(1), pages 203-218.
   3. Yamna Erraach & Fatma Jaafer & Ivana Radić & Mechthild Donner, 2021. "Sustainability Labels on Olive Oil: A Review on Consumer Attitudes and Behavior," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
   4. Teresa Del Giudice & Carla Cavallo & Francesco Caracciolo & Gianni Cicia, 2015. "What attributes of extra virgin olive oil are really important for consumers: a meta-analysis of consumers’ stated preferences," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 3(1), pages 1-15, December.

2. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.

   Cited by:

   1. Francesca Di Iorio & Umberto Triacca, 2022. "A comparison between VAR processes jointly modeling GDP and Unemployment rate in France and Germany," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 617-635, September.
   2. Steinmann, Patrick & Auping, Willem L. & Kwakkel, Jan H., 2020. "Behavior-based scenario discovery using time series clustering," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
   3. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
   4. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
   5. Umberto Triacca, 2016. "Measuring the Distance between Sets of ARMA Models," Econometrics, MDPI, vol. 4(3), pages 1-11, July.
   6. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
   7. Francesca Di Iorio & Umberto Triacca, 2014. "Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test," Econometrics, MDPI, vol. 2(4), pages 1-14, December.
   8. Alessandro De Gregorio & Stefano Iacus, 2008. "Clustering of discretely observed diffusion processes," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1077, Universitá degli Studi di Milano.
      • De Gregorio, Alessandro & Maria Iacus, Stefano, 2010. "Clustering of discretely observed diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 598-606, February.
      • Alessandro De Gregorio & Stefano Maria Iacus, 2008. "Clustering of discretely observed diffusion processes," Papers 0809.3902, arXiv.org.
   9. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
   10. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 359-376, December.
   11. Anthony Bagnall & Gareth Janacek, 2014. "A Run Length Transformation for Discriminating Between Auto Regressive Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 154-178, July.
   12. Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
   13. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
       • E. Otranto, 2008. "Identifying Financial Time Series with Similar Dynamic Conditional Correlation," Working Paper CRENoS 200817, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   14. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
   15. Bob Walrave, 2016. "Determining intervention thresholds that change output behavior patterns," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 261-278, July.
   16. Ozan Cinar & Ozlem Ilk & Cem Iyigun, 2018. "Clustering of short time-course gene expression data with dissimilar replicates," Annals of Operations Research, Springer, vol. 263(1), pages 405-428, April.
   17. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
   18. Pacifico, Antonio, 2020. "Bayesian Fuzzy Clustering with Robust Weighted Distance for Multiple ARIMA and Multivariate Time-Series," MPRA Paper 104379, University Library of Munich, Germany.
   19. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
   20. Paloma Taltavull de La Paz, 2021. "Predicting housing prices. A long term housing price path for Spanish regions," LARES lares-2021-4dra, Latin American Real Estate Society (LARES).
   21. Vilar, J.A. & Alonso, A.M. & Vilar, J.M., 2010. "Non-linear time series clustering based on non-parametric forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2850-2865, November.
   22. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
   23. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari & Dario Lallo, 2013. "Noise fuzzy clustering of time series by autoregressive metric," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 217-243, November.
   24. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
   25. E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
       • Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
   26. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
   27. Jane L. Harvill & Priya Kohli & Nalini Ravishanker, 2017. "Clustering Nonlinear, Nonstationary Time Series Using BSLEX," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 935-955, September.
   28. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 323-340, December.
   29. De Luca Giovanni & Zuccolotto Paola, 2017. "A double clustering algorithm for financial time series based on extreme events," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 1-12, June.
   30. E. Otranto, 2011. "Classification of Volatility in Presence of Changes in Model Parameters," Working Paper CRENoS 201113, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   31. Domenico Piccolo, 2012. "Discussion of “An analysis of global warming in the Alpine region based of nonlinear nonstationary time series models” by F. Battaglia and M. K. Protopapas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 363-369, August.

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-AGR: Agricultural Economics (2) 2010-05-22 2015-05-16
2. NEP-MKT: Marketing (1) 2015-05-16

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