BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Gurgul Piotr (AGH University of Science and Technology Kraków, Poland), Zając Paweł (AGH University of Science and Technology Kraków, Poland)
Tytuł
Forecasting of Migration Matrices in Business
Źródło
Statistics in Transition, 2012, vol. 13, nr 2, s. 387-404, tab., bibliogr. 41 poz.
Słowa kluczowe
Migracja, Macierze przejścia, Przedsiębiorstwo
Migration, Transition matrices, Enterprises
Uwagi
summ.
Abstrakt
This paper demonstrates that the forecast of migration matrices can be conducted by means of updating procedures, well-known in the I-O theory. The authors use some of the most popular I-O updating procedures (RAS and some non-biproportional approaches) and calculate measures of the ex-post error of predictions. While taking into account the measures of distance between two matrices, a ranking of forecasting methods of migration matrices (forecast horizon one) is established. Finally, the advantages and drawbacks of particular forecasting methods with respect to one-step ex-post forecasts of migration matrices are discussed. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. AGARWAL V., TAFFLER R. (2011). Comparing the performance of market-based and accounting based bankruptcy prediction models, Journal of Banking and Finance, 32, pp. 1541-1551.
  2. ALMON C. (1968). Recent methodological advances in input-output in the United States and Canada, Fourth International Conference on Input-Output Techniques, Geneva.
  3. ALTMAN E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, pp. 589-609.
  4. ALTMAN E., AVERY R., EISENBEIS R., SINKEY J. (1981). Application of classification techniques in business, banking, and finance, Greenwich, Conn, JAI Press.
  5. BAETGE J., STROEHER T. (2005). Empirische Insolvenzforschung zur Beurteilung der Bestandsfestigkeit von Unternehmen, in: Burmann, Chr. (Eds.), Management von Ad-hoc-Krisen: Grundlagen, Strategien und Erfolgsfaktoren, Gabler, Wiesbaden, pp. 151-167.
  6. BEAVER W.H. (1966). Financial ratios as predictors of failure, in: Empirical research in accounting: Selected studies, Supplement for Journal of Accounting Research, 4, pp. 71-111.
  7. BACHARACH M. (1970). Biproportional Matrices and Input-Output Change, Cambridge, U.K., Cambridge University Press.
  8. BLUM M. (1974). Failing Company Discriminant Analysis, Journal of Accounting Research, Spring, pp. 1 - 25.
  9. COPPENS F., VERDUYN F. (2009). Analysis of business demography using Markov chains: an application to Belgian data, Working Paper Research 170, National Bank of Belgium.
  10. DAVYDENKO S., FRANKS J. (2008). Do bankruptcy codes matter? A study of defaults in France, Germany, and the U.K., Journal of Finance, 63, pp. 565608.
  11. DEAKIN E.B. (1972). A Discriminant Analysis of Predictors of Business Failure, Journal of Accounting Research, Spring, pp. 167- 179.
  12. DIACOGIANNIS G.P. (1996). The usefulness of share prices and inflation for corporate failure prediction, University of Piraeus Journal of Economics Business, Statistics and Operations Research, 46, pp. 135-156.
  13. DIAMOND U.S. (1976). Pattern Recognition and the Detection of Corporate Failure, Ph.D. dissertation, New York University.
  14. EISENBEIS R.A. (1977). Pitfalls in the application of discriminant analysis in business, finance, and economics, Journal of Finance, 32, pp. 875-900.
  15. FREDERICK W.C., DAVIS K., POST J.E. (1988). Corporate Social Responsibility and Business Ethics, McGraw-Hill Publishing Company, New York, pp. 28-29.
  16. FOSTER R., KAPLAN S. (2001). Creative Destruction, Financial Times.
  17. GURGUL H., ZAJAC P. (2011). The dynamic model of birth and death of enterprises, Statistics in Transition, 12/ 2, pp. 381- 400.
  18. GREINER L.E., SCHEIN V.E. (1988). Power and Organization Development, Addison-Wesley.
  19. HANDY CH. (1995). The Age of Paradox, Harvard Business School Press.
  20. HESSELMANN S. (1995). Insolvenzprognose mit Hilfequalitativer Faktoren, Shaker Verlag, Aachen.
  21. JACKSON R.W., MURRAY AT. (2004). Alternative input-output matrix updating formulations, Economic Systems Research, 16, pp. 135-14.
  22. JACOBSON T., LINDE J. (2000). Credit rating and the business cycle: can bankruptcies be forecast?, Sveriges Riksbank economic review, 4, pp. 11-33.
  23. JONES S., HENSHER D A. (2008). Advances in credit risk modeling and corporate bankruptcy Prediction, University Press, Cambridge, UK.
  24. KAHL M. (2002). Economic distress, financial distress and dynamic liquidation, Journal of Finance, 57, pp. 135-168.
  25. KEASEY K., WATSON R. (1987). Non-financial symptoms and the prediction of small company failure: a test or Argenti's hypotheses, Journal of Business Finance and Accounting, 14 (3), 335-354.
  26. LAHR M., de MESNARD L. (2004). Biproportional Techniques in Input-Output Analysis: Table Updating and Structural Analysis, Economic Systems Research, 16 (2), 115-34.
  27. MATSCHKE M.J. (1979). Insolvenzprognose aus vergangenheitsorientierten Jahresabschlüssen als Basis von Kreditentscheidungen, Betriebswirtschaftlich eForschung und Praxis, 31, pp. 485-504.
  28. MILLER R.E., BLAIR P.D. (1985). Input-output analysis: Foundations and extensions, Prentice-Hall, Englewood Cliffs, N.J.
  29. MOYER R.C. (1977). Forecasting financial failure: a re-examination, Financial Management, 6, pp.11-17.
  30. MUCHE T. (2007). Ein stochastisches Modell zur Insolvenzprognose auf der Basis von Jahresabschlußdaten, Betriebswirtschaftliche Forschung und Praxis, 59, pp. 376-399.
  31. OHLSON J A. (1980). Financial Ratios and Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, Spring, pp. 109- 131.
  32. PEEL M.J., PEEL D A. (1988). A Multilogit Approach to Predicting Corporate Failure: Some Evidence for the UK Corporate Sector, Omega, pp. 309 -318.
  33. PLATT H., PLATT M. (1991). A note on the use of industry-relative ratios in bankruptcy prediction, Journal of Banking and Finance, 15, pp. 1183- 1194.
  34. ROY J., BATTEN D., LESSE P. (1982). Minimizing information loss in simple aggregation, Environment and Planning, 14, 973- 980.
  35. SHUMPETER J. (1982). The Theory of Economic Development: An inquiry into profits, capital, credit, interest and the business cycle, Transaction Publisher.
  36. STONE R.A. (1961). Input-Output Accounts and National Accounts. Paris, Organization for European Economic Cooperation.
  37. THEIL H. (1971). Applied Economic Forecasting. Amsterdam, North-Holland.
  38. THIELE O., LOHMANN K. (1995). Möglichkeiten und Grenzen der Insolvenzprognose auf der Grundlage von Jahresabschluss informationen, Freiberger Arbeitspapiere, 95/17.
  39. WILCOX J.W. (1971), A Simple Theory of Financial Ratios as Predictors of Failure, Journal of Accounting Research, Autumn, pp. 389-395.
  40. ZAVGREN C.V. (1985). Assessing the Vulnerability to the Failure of American Industrial Firms: A Logistic Analysis, Journal of Business Finance and Accounting, pp. 19-45.
  41. ŻMIJEWSKI M. (1984). Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research (Supplement), pp. 59-82.
Cytowane przez
Pokaż
ISSN
1234-7655
Język
eng
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu