IDEAS home Printed from https://ideas.repec.org/p/csc/cerisp/200509.html
   My bibliography  Save this paper

Analisi e valutazione delle performance economico-tecnologiche dei paesi e situazione italiana

Author

Abstract

National transparency and good government are important prerequisites in order to access international financial markets. In relation to the growing relevance of such issues, this study evaluates economic and technological performance of countries by discriminant analysis. The application is based on 34 countries for the 2000-2002 period. These results provide information on country risk, as well as on economic stability and growth. Some concluding remarks with a particular attention to the Italian country conclude the research.

Suggested Citation

  • Mario Coccia & Mario Taretto, 2005. "Analisi e valutazione delle performance economico-tecnologiche dei paesi e situazione italiana," CERIS Working Paper 200509, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
  • Handle: RePEc:csc:cerisp:200509
    as

    Download full text from publisher

    File URL: https://www.byterfly.eu/islandora/object/librib:358033
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    2. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. Kent D Miller, 1992. "A Framework for Integrated Risk Management in International Business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 23(2), pages 311-331, June.
    5. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    6. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Coccia, Mario & Wang, Lili, 2015. "Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 155-169.
    2. Coccia, Mario, 2018. "A Theory of the General Causes of Long Waves: War, General Purpose Technologies, and Economic Change," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 287-295.
    3. Mario Coccia, 2018. "National debts and government deficits within European Monetary Union: Statistical evidence of economic issues," Papers 1806.07830, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    2. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    3. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    4. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    5. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    6. Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
    7. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    8. Bellalah, Mondher & Zouari, Sami & Levyne, Olivier, 2016. "The performance of hybrid models in the assessment of default risk," Economic Modelling, Elsevier, vol. 52(PA), pages 259-265.
    9. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    10. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    11. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    12. Dongcheol Kim & Inro Lee, 2020. "The financial distress pricing puzzle in banking firms," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1351-1384, June.
    13. Hunter, John & Isachenkova, Natalia, 2006. "Aggregate economy risk and company failure: An examination of UK quoted firms in the early 1990s," Journal of Policy Modeling, Elsevier, vol. 28(8), pages 911-919, November.
    14. Evangelos C. Charalambakis, 2015. "On the Prediction of Corporate Financial Distress in the Light of the Financial Crisis: Empirical Evidence from Greek Listed Firms," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 22(3), pages 407-428, November.
    15. Frieda Rikkers & Andre E. Thibeault, 2009. "A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 229-264, September.
    16. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.
    17. Inekwe, John Nkwoma & Jin, Yi & Valenzuela, Ma. Rebecca, 2018. "The effects of financial distress: Evidence from US GDP growth," Economic Modelling, Elsevier, vol. 72(C), pages 8-21.
    18. Vo, D.H. & Pham, B.V.-N. & Pham, T.V.-T. & McAleer, M.J., 2019. "Corporate Financial Distress of Industry Level Listings in an Emerging Market," Econometric Institute Research Papers EI2019-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Viral Acharya & Sergei A. Davydenko & Ilya A. Strebulaev, 2012. "Cash Holdings and Credit Risk," The Review of Financial Studies, Society for Financial Studies, vol. 25(12), pages 3572-3609.
    20. Alexandros Benos & George Papanastasopoulos, 2005. "Extending the Merton Model: A Hybrid Approach to Assessing Credit Quality," Finance 0505020, University Library of Munich, Germany, revised 18 Nov 2005.

    More about this item

    Keywords

    Country evaluation; Economic performance; Comparative analysis of countries; Technological indicator; Discriminant analysis;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:csc:cerisp:200509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anna Perin or Giancarlo Birello (email available below). General contact details of provider: https://edirc.repec.org/data/cerisit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.