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Modelowanie i zarządzanie efektami transferu dynamiki kursów wymiany na ceny przedsiębiorstw budowy maszyn na Ukrainie
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Abstrakty
The article is devoted to the assessment of the transfer of exchange rates to domestic prices for the products of machine-building enterprises in Ukraine. The study found that the main reason for transferring the dynamics of exchange rates on the prices for the products of machine-building enterprises of Ukraine is a change in production costs for raw materials, resources, and a change in exchange rates. As a model for assessing the degree of transfer of currency rates to the prices of engineering enterprises were chosen the autocorrelation method and the predictive ARIMA model. The ARIMA model allowed detected a time gap between the change in the exchange rate indices and the change in domestic prices for products of Ukrainian machine-building enterprises. It was proposed to take into account in the process of pricing a new factor of influence - "time factor", which takes place in the calculation of prices taking into account the effect of the transfer of exchange rate changes. It was proposed indicators of modified price elasticity coefficients for engineering products depending on the rate of change in exchange rates. The aim of the research is to develop a methodology for modelling and managing the effect of shifting the dynamics of the exchange rates on the prices of the enterprises of machine-building in Ukraine. The main factors that increase the dependence of domestic prices on products of machine-building enterprises from exchange rates are: liberalization of the economy and openness of the machine-building industry for foreign markets; dependence of the raw material and resource base on imported components; increase in the export of machine-building products; weak price differentiation of production of machine-building enterprises.
Artykuł poświęcony jest ocenie transferu kursów walutowych na ceny krajowych produktów przedsiębiorstw budowy maszyn na Ukrainie. Badanie wykazało, że głównym powodem przeniesienia dynamiki kursów wymiany na ceny produktów przedsiębiorstw budowy maszyn na Ukrainie jest zmiana kosztów produkcji surowców, zasobów, zmiana kursów walut. Jako model do oceny stopnia transferu kursów walut do cen przedsiębiorstw inżynieryjnych wybrano metodę autokorelacji i predykcyjny model ARIMA. Model ARIMA pozwolił wykryć lukę czasową między zmianą wskaźników kursu walutowego a zmianą cen krajowych produktów ukraińskich przedsiębiorstw budowy maszyn. Zaproponowano uwzględnienie w procesie wyceny nowego czynnika wpływu - „czynnika czasu”, który ma miejsce przy obliczaniu cen z uwzględnieniem efektu przeniesienia zmian kursu walutowego. Zaproponowano wskaźniki zmodyfikowanych współczynników elastyczności cen dla produktów inżynieryjnych w zależności od tempa zmian kursów wymiany. Celem badań jest opracowanie metodologii modelowania i zarządzania efektem transferu dynamiki kursów walutowych na ceny przedsiębiorstw budowy maszyn na Ukrainie. Głównymi czynnikami zwiększającymi zależność cen krajowych od produktów przedsiębiorstw przemysłu maszynowego od kursów walutowych są: liberalizacja gospodarki i otwartość przemysłu budowy maszyn na rynki zagraniczne; zależność surowców i zasobów od importowanych komponentów; wzrost eksportu produktów do budowy maszyn; słabe zróżnicowanie cenowe produkcji przedsiębiorstw budowlanych.
Czasopismo
Rocznik
Tom
Strony
117--129
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
- Marketing department, Odessa National Polytechnic University, Odessa, Ukraine
autor
- Department of Accounting and Taxation, Odessa I.I. Mechnikov National University, Odessa, Ukraine
autor
- Kharkiv Petro Vasylenko National Technical University of Agriculture, Kharkiv, Ukraine
autor
- Kharkiv Petro Vasylenko National Technical University of Agriculture, Kharkiv, Ukraine
autor
- Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania
autor
- Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania
Bibliografia
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- 3. Bannister M.G.J., Turunen M.J., Gardberg M., 2018, Dollarization and Financial Development, International Monetary Fund.
- 4. Bocola L., Lorenzoni G., 2017, Financial Crises, Dollarization, and Lending of Last Resort in Open Economies, (No. 23984), National Bureau of Economic Research.
- 5. Borensztein M.E., Berg M.A., 2000, The pros and cons of full dollarization, International Monetary Fund.
- 6. Caglayan M., Talavera O., 2016, Dollarization, liquidity and performance: Evidence from Turkish banking.
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- 8. Corbo V., 2017, Exchange rate and monetary regimes, [In:] Beyond Transition (pp. 61-73), Routledge.
- 9. Dekker M., 2017, From macro to micro: how smallholder farmers in Zimbabwe are coping with dollarization, “ASCL Occasional Publications”, 12.
- 10. Delatte A.-L., López-Villavicencio A., 2012, Asymmetric exchange rate pass-through: Evidence from major economies, “Journal of Macroeconomics”, 34(30.
- 11. Devereux M., Lane P., Xu J., 2006, Exchange rates and monetary police in emerging market economies, “Economic Journal”, 116 (511).
- 12. Dobrynskaya V.V., 2008, The monetary and exchange rate policy of the Central Bank of Russia under asymmetrical price rigidity, “Journal of Innovations Economics”, 1(1).
- 13. Frankel J.A., 2012, Choosing an Exchange Rate Regime, [In:] J. James, I.W. Marsh, L. Sarno, Handbook of Exchange Rates, (pp. 761-82), Hoboken, New Jersey: John Wiley & Sons, Inc.
- 14. Goldberg P.K., Knetter M.M., 1997, Goods Prices and Exchange Rates: What have we Learned., “Journal of Economic Literature”, XXXV.
- 15. Guha B., Bandyopadhyay G., 2016, Gold price forecasting using ARIMA model, “Journal of Advanced Management Science”, 4(2).
- 16. Horton M.M.A., Samiei H., Epstein M.N.P., Ross M.K., 2016, Exchange Rate Developments and Policies in the Caucasus and Central Asia, International Monetary Fund.
- 17. Jaber M., Jaber K., 2017, Currency Substitution and Price Endings: Right Digit Effect, “Journal of Global Marketing”, 30(4).
- 18. Jadhav V., Chinnappa Reddy B.V., Gaddi G.M., 2017, Application of ARIMA model for forecasting agricultural prices, “Journal of Agricultural Science and Technology”, 19(5).
- 19. Kumar S.V., Vanajakshi L., 2015, Short-term traffic flow prediction using seasonal ARIMA model with limited input data, “European Transport Research Review”, 7(3).
- 20. Liu L., Luan R.S., Yin F., Zhu X.P., Lü Q., 2016, Predicting the incidence of hand, foot and mouth disease in Sichuan province China using the ARIMA model, “Epidemiology & Infection”, 144(1).
- 21. Malindretos J., Tsanacas D., 2019, A Policy Orientation of the Criticisms of the Traditional Theory of International Finance in the Context of Fixed Exchange Rates, “Quantity and Quality in Economic Research: Studies in Applied Business Research”, 4.
- 22. Marí Del Cristo M. L., Gómez-Puig M., 2016, Fiscal sustainability and dollarization: the case of Ecuador, “Applied Economics”, 48(23).
- 23. Michalchuk N.A., 2016, Dedollarization policy in foreign countries, “Economy and Management”, 1(45).
- 24. Ministry of Finance of Ukraine, (2018), Retrieved from https://www.minfin.gov.ua.
- 25. Ozturk S., Ozturk F., 2018, Forecasting energy consumption of Turkey by Arima model, “Journal of Asian Scientific Research”, 8(2).
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- 27. Pilossof R., 2009, ‘Dollarisation’ in Zimbabwe and the Death of an Industry, “Review of African Political Economy”, 36(120).
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- 29. Samreth S., Sok P., 2018, Revisiting the Impacts of Exchange Rate Movement on the Dollarization Process in Cambodia.
- 30. Semenov S.K., 2007, Money: Dollarization as an Inflation Factor, “Finance and Credit”, 30(270).
- 31. Surkala I.I., Nicholas J., 2016, To Trade or Not to Trade: Public Opinion and Trade Liberalization in Chile.
- 32. Valipour M., 2015, Long-term runoff study using SARIMA and ARIMA models in the United States, “Meteorological Applications”, 22(3).
- 33. Vásquez C.Á., Sornoza V.F.G., Ponce L.D.J.A., Vásquez A.E.P., Zambrano D.A., Chilán J.H.M., 2018, Cost-benefit analysis of dollarization, Ecuador case, “Dominio de las Ciencias”, 4(4).
- 34. Versal N., Stavytskyy A., 2015, Financial dollarization: Trojan horse for Ukraine? “Ekonomika”, 94(3).
- 35. Wang W.C., Chau K.W., Xu D.M., Chen X.Y., 2015, Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition, “Water Resources Management”, 29(8).
- 36. Yashkina O., 2016, Determination of risks in pricing by regression model of dependence of demand on price, “Marketing and Management Innovation”, 3.
- 37. Yuan C., Liu S., Fang Z., 2016, Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model, “Energy”, 100.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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