Advertisement

Methods and Mathematical Models of Budget Management

Chapter
  • 1.8k Downloads

Abstract

The methods and mathematical models for program control of budget resources focused on the end result such as correct planning were developed according to the strategic plan of socio-economic development of the country/region. The end result is used as a landmark which enables to estimate achievability of the predetermined level under certain limitations in budget funds and budget potential determined for the moment of the medium-term planning. The chapter presents the method estimating stability of program movements determining system decisions based on construction of Lyapunov’s function and allowing to estimate efficiency of budget mechanism of resource distribution. The principles of designing of an intellectual system modeling program control of budget resources and enabling to correct the obtained decision by adjustment of the system of indicators are described.

Keywords

Budget Process Budget Expenditure Budget Planning Budget Resource Budget System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Implementation of the middle-term financial planning in the budget process of the Russian Federation. Report of the Fiscal Policy Center. Contract OUT-PER-I-00-99-00003-00 (2002) Google Scholar
  2. 2.
    Serpilin: The Great Britain experience in regional management and result-oriented budgeting: materials of the “International Cooperation” program. http://www.unicom-ms. 10 March 2006
  3. 3.
    Melkers, J., Willoughby, K.: The state of the states: performance-based budgeting requirements in 47 out of 50. Public Adm. Rev. 58(1), 66–73 (1998). doi: 10.2307/976891 CrossRefGoogle Scholar
  4. 4.
    Lee, J.Y.-J., Wang, X.: Assessing the impact of performance-based budgeting: a comparative analysis across the United States, Taiwan, and China. Public Adm. Rev. 69, 60–66 (2009) CrossRefGoogle Scholar
  5. 5.
    Result-oriented budgeting: international experience and possibilities of its use in Russia. Report of the Fiscal Policy. Contract OUT-PER-I-00-99-00003-00 (2002) Google Scholar
  6. 6.
    Mutanov, G.M., Mamykova, Zh.D.: Methods and Mathematical Models of the Software Budget Management. University, Almaty (2008), p. 144. ISBN 978-601-208-014-8 Google Scholar
  7. 7.
    Bruins, R.J.F., Munns, W.R. Jr., et al.: A new process for organizing assessments of social, economic, and environmental outcomes: case study of wildland fire management in the USA. Integr. Environ. Assess. Manag. 6, 469–483 (2010) Google Scholar
  8. 8.
    Narver, J.C., Slater, S.F.: The effect of a market orientation on business profitability. J. Mark. 54, 20–35 (1990) CrossRefGoogle Scholar
  9. 9.
    Hurley, R.F., Hult, G.T.M.: Innovation, market orientation and organizational learning: an integration and empirical examination. J. Mark. 62, 42–54 (1998) CrossRefGoogle Scholar
  10. 10.
    Mutanov, G.M., Kulikova, V.P.: Mathematical Modeling of Economic Processes. Economics, Almaty (1999), p. 356 Google Scholar
  11. 11.
    Intriligator M. Mathematical Methods of Optimization and Economic Theory. Progress (1975), p. 607. Translation from English Google Scholar
  12. 12.
    Sovetov, B.Ya., Yakovlev, S.Ya.: Modeling of Systems. Moscow (1985), p. 239 Google Scholar
  13. 13.
    The Theory of Forecasting and Decision Making: A Teaching Aid. Higher School (1977) Google Scholar
  14. 14.
    Neimark, Yu.I., Kogan, N., Savelyev, V.: Dynamic Models of Management Theory. Nauka, Moscow (1985), p. 400. Main editorial of physico-mathematical literature Google Scholar
  15. 15.
    Zubov, V.I.: Lectures on Control Theory. Nauka, Moscow (1975) Google Scholar
  16. 16.
    Eliseyeva, I.I.: Econometrics. Moscow (2003) Google Scholar
  17. 17.
    Dougerti, K.: Introduction to Econometrics. INFRA-M, Moscow (1999), p. 402. Translated from English Google Scholar
  18. 18.
    Chernyak, Y.A.: System Analysis in Economic Control. Economics, Moscow (1975), p. 191 Google Scholar
  19. 19.
    Yudin, D.B., Yudin, A.D.: Extreme Models in Economics. Economics, Moscow (1979), p. 288 Google Scholar
  20. 20.
    Shelobayev, S.I.: Mathematical Methods and Models. Moscow (2001) Google Scholar
  21. 21.
    Kholod, N.I., Kuznetsov, A.V., Zhikhar, Y.N., et al.: Economic and Mathematical Methods and Models. BGEU (2000), p. 412 Google Scholar
  22. 22.
    Sulitsky, V.N.: Methods of Statistical Analysis in Management. Moscow (2002) Google Scholar
  23. 23.
    Lewis, K.D.: Medium-Term Forecasting. Moscow (1993) Google Scholar
  24. 24.
    Teece, D.J., Pisano, G.P., Shuen, A.: Dynamic capabilities and strategic management. Strateg. Manag. J. 18, 509–533 (1997) CrossRefGoogle Scholar
  25. 25.
    Yefimova, M.P., Petrova, E.V., Rumyantsev, V.N.: General Theory of Statistics. Moscow (2004) Google Scholar
  26. 26.
    Zhdanov, S.A.: Economic Models and Methods in Management. Business and Service, Moscow (1988), p. 176 Google Scholar
  27. 27.
    Dubrova, T.A.: Statistical Methods in Forecasting. UNITY, Moscow (2003) Google Scholar
  28. 28.
    Mutanov, G.M., Bykova, I.Yu., Mamykova, Zh.D.: Software control of budget funds. Bull. Natl. Acad. Eng. Repub. Kaz. 4(26), 33–38 (2007) Google Scholar
  29. 29.
    Mutanov, G.M., Shintemirova, A.U.: Mathematical Budget Models. Astana (2003), p. 176 Google Scholar
  30. 30.
    Afanasyev, V.N., Kolmanovskii, V.B., Nosov, V.R.: Mathematical Theory of Constructing Control Systems. “High School” (1998), p. 574 Google Scholar
  31. 31.
    Odintsov, B.E.: Designing Economic Expert Systems: Teaching Aid for Higher Education Institutions. Computer, UNITY (1996), p. 166 Google Scholar
  32. 32.
    Heerdt, J.A., Coutinho, D.F., Mussa, S.A., Heldwein, M.L.: Control strategy for current harmonic programmed AC active electronic power loads. IEEE Trans. Ind. Electron. 61(8), 3810–3822 (2014) CrossRefGoogle Scholar
  33. 33.
    Adi, V.S.K., Chang, C.-T.: A mathematical programming formulation for temporal flexibility analysis. Comput. Chem. Eng. 57, 151–158 (2013) CrossRefGoogle Scholar
  34. 34.
    Grigoroudis, E., Phillis, Y.A.: Modeling healthcare system-of-systems: a mathematical programming approach. IEEE Syst. J. 7(4), 571–580 (2013) CrossRefGoogle Scholar
  35. 35.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985) CrossRefGoogle Scholar
  36. 36.
    Rachel, F.M., Cugnasca, P.S.: The multi-agent programming paradigm use for railway applications. In: Allan, J., Arias, E., Brebbia, C.A., Goodman, C., Rumsey, A.F., Sciutto, G., Tom, N. II (eds.) Computers in Railways XI: Computer System Design and Operation in the Railway and Other Transit Systems, vol. 103, pp. 641–650 (2008) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Al-Farabi Kazakh National UniversityAlmatyKazakhstan

Personalised recommendations