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Прогнозування Реакції Економіки України На Економічні Шоки В Сусідніх Державах: Глобальна Векторна Авторегресійна Модель «Україна-Сусіди»
[Forecasting the Responses of Ukraine to Economic Shocks in the Neighbour-Countries: Global Vector Autoregressive Model “Ukraine-Neighbours”]


  • Matkovskyy, Roman


In this article the approach of Global Vector-Autoregressive (GVAR) models has been applied to Ukraine and its neighbour-countries which contiguous to Ukraine: Belarus, Bulgaria, Georgia, Moldova, Romania, Poland, Slovakia, Russia Federation, Turkey and Hungary. The goal of the research is to identify and forecast interactions among these economies, estimate import-export flows, discover the mechanism of the response of Ukraine to inflation and unemployment shocks and also of the shocks transmission mechanism to Ukrainian economy.

Suggested Citation

  • Matkovskyy, Roman, 2012. "Прогнозування Реакції Економіки України На Економічні Шоки В Сусідніх Державах: Глобальна Векторна Авторегресійна Модель «Україна-Сусіди»
    [Forecasting the Responses of Ukraine to Economic Shocks in
    ," MPRA Paper 44717, University Library of Munich, Germany, revised Nov 2012.
  • Handle: RePEc:pra:mprapa:44717

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    References listed on IDEAS

    1. MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 563-577, Sept.-Oct.
    2. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    3. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 642-675, October.
    4. Matthew Greenwood‐Nimmo & Viet Hoang Nguyen & Yongcheol Shin, 2012. "Probabilistic forecasting of output growth, inflation and the balance of trade in a GVAR framework," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(4), pages 554-573, June.
    5. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    6. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    7. Bussière, Matthieu & Chudik, Alexander & Sestieri, Giulia, 2009. "Modelling global trade flows: results from a GVAR model," Working Paper Series 1087, European Central Bank.
    8. Silvia Sgherri & Alessandro Galesi, 2009. "Regional Financial Spillovers Across Europe; A Global VAR Analysis," IMF Working Papers 09/23, International Monetary Fund.
    9. Christian Dreger & Yanqun Zhang, 2011. "The Chinese Impact on GDP Growth and Inflation in the Industrial Countries," Discussion Papers of DIW Berlin 1151, DIW Berlin, German Institute for Economic Research.
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    More about this item


    global vector autoregressive model (GVAR); impulse response function; shocks transmission mechanism;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications


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