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Sanja Vlaović Begović, Mirela Momčilović and Stevan Tomašević

References Altman, E.I. (1968) “Financial Ratios. Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, 23(4): 589-609. Altman, E.I. (1983) Corporate Financial Distress, New York, Wiley InterScience. Altman, E.I., Hartzell, J. and Peck, M. (1995) Emerging Markets Corporate Bonds: A Scoring System, Salomon Brothers Inc. New York. Altman, E.I. and Hotchkiss, E. (2006) Corporate Financial Distress & Bankruptcy, 3 rd edition, Hoboken, NJ, J. Wiley & Sons. Altman, E.I., Danovi, A. and Falini, A

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Laura Rotar

Economics , 15, (3), pp. 507-528. Becker, S. O., Ichino, A. (2002), "Estimation of Average Treatment Effects Based on Propensity Scores." The Stata Journal , 2, (4), pp. 358-377. Briggs, D. C. (2004), "Causal Inference and the Heckman Model." Journal of Education and Behavioral Statistics , 29, pp. 397-420. Caliendo, M., Hujer, R. (2006), "The Microeconometric Estimation of Treatment Effects. An Overview." Allgemeines Statistisches Archiv , 90, pp. 197-212. Cameron

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Eric Anane and Jephtar Adu-Mensah

References Allen, J., Gregory, A., Mikami, A., Lun, J., Hamre, B., & Pianta, R. (2013). Observations of effective teacher–student interactions in secondary school classrooms: Predicting student achievement with the classroom assessment scoring system - secondary. School Psychology Review , 42 (1), 76. Anane, E. (2011). Predictive validity of continuous assessment scores of students’ performance in end-of-semester examinations in colleges of education in Ghana. Journal of Business and Educational Policies, 7 (1), 95-104. Black, P., Harrison, C

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Shoaib Abdul Basit, Thomas Kuhn and Mumtaz Ahmed

+ D de las empresas: un análisis empírico”, Herri ekonomiaz, No. 11, pp. 47-66. 13. Cainelli, G., Evangelista, R., Savona, M. (2004), „The impact of innovation on economic performance in services”, The Service Industries Journal, Vol. 24, No. 1, pp. 116-130. 14. Caliendo, M., Kopeinig, S. (2008), “Some Practical Guidance for the Implementation of Propensity Score Matching”, Journal of Economic Surveys, Vol. 22, No. 1, pp. 31-72. 15. Carboni, O. A. (2017), “The effect of public support on investment and R&D: An empirical evaluation on European

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Kiril Postolov, Ivan Milenkovic, Dragana Milenkovic and Aleksandra Janeska Iliev

References: 1. Al-Sulaiti, K.I., Almwajeh, O. (2007), “Applying Altman Z-score model of bankruptcy on service organizations and its implications on marketing concepts and strategies”, Journal of International Marketing & Marketing Research, Vol.32, No.2, pp. 59-74. 2. Altman, E., (1968), “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance , vol.34, No. 4, pp.589-609. 3. Altman, E.I. (1968), “ Financial ratios, discriminant analysis and prediction of corporate bankruptcy”, Journal of

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Cristina Boboc and Oana Calavrezo

. Heckman., J., Ichimura, H., Todd, P. 1998. "Matching as an econometric evaluation estimator", Review of Economic Studies , 65(2):261-294. Hirschman, A. 1970. Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States , Cambridge, MA, Harvard University Press. Leuven, E., Sianesi, B. 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing". http://ideas.repec.org/c/boc/bocode/s432001.html

Open access

Rabeb Kammoun

Abstract

Maritime transportation for Tunisia plays an important role in trade exchange with other countries. Therefore, the objective of this paper is to measure the efficiency scores of 7 seaports in Tunisia by applying the Stochastic Frontier Analysis (SFA) with Cobb-Douglas production function and Data envelopment analysis (DEA) with CCR and BCC models. The annual data collected cover the 2007-2017 period for each port. Thus, the sample size for the analysis comprises a total of 77 observations. The empirical result shows that the total average scores of operating efficiency scores were DEA-BCC (0.746) >SFACD (0.536)>DEA-CCR (0.334) from 2007 to 2017. Given these results, the port of Gabes can be considered as the best efficient port in the 3 models (DEA-BCC, DEA-CCR and SFA-CD).

Open access

Andrija Đurović

References 1. Allen, L. N. and Rose, L. C. (2006), Financial survival analysis of defaulted debtors, Journal of Operational Research Society, 57, 630-636. 2. Baba, N. and Goko, H. (2006), Survival analysis of hedge funds, Bank of Japan, Working Papers Series No. 06er S 3. Belkin, B., Suchower, S., Forest, L. R. (1998), A one-parameter representation of credit risk and transition matrices. Working paper, KPMG Peat Marwick LLP 4. Breeden, J. L. (2010), Reinventing Retail Lending Analytics: Forecasting, Stress Testing, Capital, and Scoring

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Andrija Đurović

.R. and Hess, J.L. Statistical Design and Analysis of Experiments with Application to Engineering and Science, Wiley-Interscience, 2003 14. Milne, A., Parboteeah, P (2016) The Business Models and Economics of Peer-to-Peer Lending, European Credit Research Institute (ECRI), Research Report No 17 15. Narain, B. (1992): Survival Analysis and the Credit Granting Decision. In: Thomas, L. C. – Crook, J. N. – Edelman, D. B. (eds): Credit Scoring and Credit Control. Oxford, Oxford University Press, 1992, 109-122. 16. Roszbach, K. (2003). Bank lending policy

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Lyudmyla Malyarets, Oleksandr Dorokhov, Vitaliya Koybichuk and Liudmyla Dorokhova

Sector Challenges in Research. Journal of Applied Management and Investments , 5 (1), 34-39. 16. Hooman, A., Marthandan, G., Yusoff, W. & Omid, M. (2016). Statistical and data mining methods in credit scoring. Journal of Developing Areas , 50 (5), 371-381. 17. Koybichuk, V. (2013). Formation of models for features space of bank competitiveness [Formuvannya oznakovoho prostoru modeli konkurentospromozhnosti banku]. Bulletin of Khmelnytsky National University , 4 (1), 173–179. 18. Koybichuk, V. (2012). Conceptual model of the bank