Forecasting GDP growth rate in Ukraine with alternative models

• Autorzy: Karayuz I , Bidyuk P.
• Języki publikacji: EN

Abstrakty

EN

The problem of constructing mathematical model for short-term fore-casting of GDP is considered. First, extended autoregression is constru-cted that takes two additional independent variables into consideration. The model resulted provides a possibility for generating short-term forecasts of GDP though not of high quality. Another model was constructed in the form of a Bayesian network. The model turned out to be better than the multiple regression, it provides quite good estimates for probabilities of GDP growth direction.

Słowa kluczowe

EN

Regressive model; Bayesian network; short-term forecasting; GDP of Ukraine

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2015
• Tom: Vol. 11, no 3
• Strony: 88--97
• Opis fizyczny: Bibliogr. 10 poz., fig., tab.

Twórcy

autor

Karayuz I

• Postgraduate student, National Technical University of Ukraine “Kyiv Polytechnic Institute”, Institute for Applied System Analysis, 03056, building № 35, 37 Prosp. Peremohy, NTUU “KPI”, Kiev, Ukraine

autor

Bidyuk P.

• National Technical University of Ukraine “Kyiv Polytechnic Institute”, Institute for Applied System Analysis, 03056, building № 35, 37 Prosp. Peremohy, NTUU “KPI”, Kiev, Ukraine

Bibliografia

• [1] Hoogstrate A., Palm F., Pfann G.: Pooling in Dynamic Panel-Data Models: An Application to Forecasting GDP Growth Rates. Journal of Business & Economic Statistics, 2000, Vol. 18, No. 3, pp. 274–283.
• [2] Forni M., Hallin M., Lippi M., Reichlin L.: The Generalized Dynamic-Factor Model: Identification and Estimation. The Review of Economics and Statistics, 2000, Vol. 82, No. 4, pp. 540–554.
• [3] Banbura M., Rünstler G.: A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP. International Journal of Forecasting, 2011, Vol. 27, No. 2, pp. 333–346.
• [4] Branson W.: Macroeconomic theory and policy. New York: Harper & Row Publishers, 1989, p. 656.
• [5] Delurgio S.A.: Forecasting principles and applications. McGraw-Hill, Boston 1998, p. 802.
• [6] Rutkowski L.: Metody i techniki sztucznej inteligencji. Wydawnictwo Naukowe PWN, Warszawa 2005, p. 520.
• [7] Pole A., West M., Harrison J.: Applied Bayesian forecasting and time series analysis. Chapman & Hall/CRC, New York 1994, p. 409.
• [8] Macroeconomics / Ed. by V.D. Basilevych, Znannya, Kyiv 2007, p. 703.
• [9] The State Statistical Service of Ukraine: http://www.ukrstat.gov.ua
• [10] Zgurowsky M.Z., Bidyuk P.I., Terentyev O.M., Prosyankina T.I.: Bayesian networks and decision trees. Edelveis, Kyiv 2015, p. 360.