Оld version

Home » Publications » Visnyk of the National Bank

How Trade Composition Affects Sensitivity to Foreign Shocks: Applying a Global VAR Model to Ukraine



Visnyk of the National Bank of Ukraine, 2019, No. 247, pp. 4-18

https://doi.org/10.26531/vnbu2019.247.01


How Trade Composition Affects Sensitivity to Foreign Shocks: Applying a Global VAR Model to Ukraine

Oleksandr Farynaab , Heli Simolac

a National Bank of Ukraine, Kyiv, Ukraine
b National University of Kyiv-Mohyla Academy, Kyiv, Ukraine
c The Bank of Finland Institute for Economies in Transition, BOFIT, Helsinki, Finland



Abstract

This paper studies the transmission of foreign output shocks to real activity in Ukraine through international trade. We employ a global vector auto regressive (GVAR) model that captures about 80% of the world economy and incorporates time-varying trade and financial weights. According to our estimates, a mild recession in the US of a 1% drop in output generates a substantial recession in Ukraine of about 2.2%. A similar drop of output in the euro area and Russia translates to a drop in output of about 1.7% in Ukraine. Finally, the same drop of output in CEE, China, or the CIS leads to an output decline of about 0.4% in Ukraine. Meanwhile, Ukraine’s response to euro area output shock has been steadily increasing over the last couple of decades due to changes in global trade flows. Ukraine’s sensitivity to shocks in the US and euro area is notably strengthened by indirect trade effects, while the response to shocks from emerging economies, i.e., China, CEE, the CIS, and partially Russia, is mainly determined by bilateral trade linkages.


JEL Codes: C32, F42, F43, E32

Keywords: Ukraine, global VAR, foreign shocks, trade compositions

Full text (PDF)


Citation: Faryna, O., Simola, H. (2019). How trade composition affects sensitivity to foreign shocks: Applying a global VAR model to Ukraine. Visnyk of the National Bank of Ukraine, 247, 4-18. https://doi.org/10.26531/vnbu2019.247.01

Citation Format:


References


Alturki, F., Espinosa-Bowen, J., Ilahi, N. (2009). How Russia affects the neighborhood: trade, financial and remittance channels. IMF Working Paper, 09/277. International Monetary Fund. https://doi.org/10.5089/9781451874228.001


Beckmann, E., Fidrmuc, J. (2013). Exchange rate pass-through in CIS countries. Comparative Economic Studies, 55(4), 705-720. https://doi.org/10.1057/ces.2013.8


Canova, F., Ciccarelli, M. (2013). Panel vector autoregressive models: A survey. European Central Bank Working Paper Series, 1507. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1507.pdf


Chudik, A., Pesaran, M. H. (2013). Econometric analysis of high dimensional VARs featuring a dominant unit. Econometric Reviews, 32(5-6), 592-649. https://doi.org/10.1080/07474938.2012.740374


Comunale, M., Simola, H. (2018). The pass-through to consumer prices in CIS economies: The role of exchange rates, commodities and other common factors. Research in International Business and Finance, 44, 186-217. https://doi.org/10.1016/j.ribaf.2017.07.076


Dees, S., di Mauro, F., Pesaran, M. H., Smith, L. V. (2007). Exploring the international linkages of the euro area: A global VAR analysis. Journal of Applied Econometrics, 22(1), 1-38. https://doi.org/10.1002/jae.932


Dreger, C., Fidrmuc, J. (2011). Drivers of exchange rate dynamics in selected CIS Countries: Evidence from a Factor-Augmented Vector Autoregressive (FAVAR) Analysis. Emerging Markets Finance and Trade, 47(4), 49-58. https://doi.org/10.2753/ree1540-496x470403


Faryna, O. (2016a). Exchange rate pass-through and cross-country spillovers: some evidence from Ukraine and Russia. BOFIT Discussion Paper, No. 14. Helsinki: Bank of Finland. https://helda.helsinki.fi/bof/bitstream/handle/123456789/14368/dp1416.pdf


Faryna, O. (2016b). Nonlinear exchange rate pass-through to domestic prices in Ukraine. Visnyk of the National Bank of Ukraine, 236, 30-42. National Bank of Ukraine. https://doi.org/10.26531/vnbu2016.236.030


Faryna, O., Simola, H. (2018). The transmission of international shocks to CIS economies: A Global VAR Approach. NBU Working Paper, 4/2018. https://bank.gov.ua/doccatalog/document?id=77136251


Feldkircher, M. (2015). A global macro model for emerging Europe. Journal of Comparative Economics, 43(3), 706-726. https://doi.org/10.1016/j.jce.2014.09.002


Feldkircher, M. Korhonen, I. (2014). The rise of China and its implications for emerging markets – evidence from a GVAR model. Pacific Economic Review, 19(1), 61-89. https://doi.org/10.1111/1468-0106.12052


Galesi, A., Lombardi, M. J. (2009). External shocks and international inflation linkages: a global analysis. European Central Bank Working Paper Series, 1062. Frankfurt am Main: European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1062.pdf


Hajek J., Horvath R. (2018). International spillovers of (un)conventional monetary policy: the effect of the ECB and the US Fed on non-euro EU countries. Economic Systems, 42(1), 91-105. https://doi.org/10.1016/j.ecosys.2017.10.001


Harahap, B. A., Bary, P., Panjaitan, L. N., Satyanugroho, R. (2016). Spillovers of United States and people’s republic of China shocks on small open economies: the case of Indonesia. ADBI Working Paper Series, 616. https://www.adb.org/sites/default/files/publication/213516/adbi-wp616.pdf


Lepushynskyi, V. (2015). A strategic document on monetary policy for the period of the inflation targeting adoption in Ukraine. Visnyk of the National Bank of Ukraine, 233, 24-38. https://doi.org/10.26531/vnbu2015.233.024


Pesaran, M. H., Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. https://doi.org/10.1016/s0165-1765(97)00214-0


Pesaran, M. H., Schuermann, T., Weiner, S. M. (2004). Modelling regional interdependencies using a global error-cointegration macro-econometric model. Journal of Business & Economic Statistics, 22, 129-162. https://doi.org/10.1198/073500104000000019


Smith, L. V., Yagamata, T. (2011). Firm level return-volatility analysis using dynamic panels. Journal of Empirical Finance, 18(5), 847-867. https://doi.org/10.1016/j.jempfin.2011.07.001


Smith, L. V., Galesi, A. (2014). GVAR Toolbox 2.0. Retrieved from https://sites.google.com/site/gvarmodelling/gvar-toolbox






This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

  Top