1. bookVolumen 22 (2022): Heft 1 (June 2022)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1804-1663
Erstveröffentlichung
19 Feb 2010
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

Modelling Determinants of Inflation in CESEE Countries: Global Vector Autoregressive Approach

Online veröffentlicht: 23 Jun 2022
Volumen & Heft: Volumen 22 (2022) - Heft 1 (June 2022)
Seitenbereich: 137 - 169
Eingereicht: 08 Dec 2021
Akzeptiert: 29 Apr 2022
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1804-1663
Erstveröffentlichung
19 Feb 2010
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Abstract

After a prolonged period of relatively stable price levels, the beginning of the third decade of the 21st century has brought inflation once again into the spotlight. This paper focuses on the inflation dynamics in a set of post-communist countries that eventually became members of the European Union. Due to EU accession augmented by the globalization process and involvement in global value chains (GVC), the international impacts are becoming progressively important for the domestic inflation dynamics and domestic variables are not sufficient to fully describe the domestic inflation dynamics. The employed methodology, Global Vector Autoregressive (GVAR) approach, allows modelling interactions and spillovers among countries, making the most of its advantages over the usual VAR models that model each economy separately and panel models, where countries are often treated as independent units. The results of the empirical analysis confirm that the globalisation process has led to increasing the importance of international impacts on the domestic inflation dynamics. On the other hand, the results also indicate that accounting for a larger set of countries decreases the severity of the commodity price shocks and makes them less persistent. Furthermore, monetary policy acts as a buffer against adverse shocks, especially in the countries that are still not members of the euro-zone. The findings of the paper show that the analysed countries are pronouncedly heterogeneous. Hence, each of the analysed economies has its own set of country-specific factors which, from country to country, play a more important or a less significant role in explaining national inflation dynamics. Thus, the paper should contribute to a more comprehensive understanding of the inflation dynamics in the policy-making context.

JEL Classification

ALBULESCU, C. T., OROS, C., TIWARI, A. K. (2017). Oil price–inflation pass-through in Romania during the inflation targeting regime. Applied Economics, 49(15), 1527-1542. DOI: https://doi.org/10.1080/00036846.2016.1221041 Search in Google Scholar

AUER, R., BORIO, C., FILARDO, A. (2017). The Globalisation of Inflation: The Growing Importance of Global Value Chains (BIS Working Paper 602). Basel: Bank for International Settlements. Search in Google Scholar

BACKE, P., FELDKIRCHER, M., SLAČIK, T. (2013). Economic Spillovers from the Euro Area to the CESEE Region via the Financial Channel: A GVAR Approach. Focus on European Economic Integration, No. 4, 50-64. Search in Google Scholar

BOTRIĆ, V., COTA, B. (2006). Sources of inflation in transition economy: The case of Croatia. Ekonomski pregled, 57(12), 835-854. Search in Google Scholar

BURRIEL, P., GALESI, A. (2018). Uncovering the Heterogeneous Effects of ECB Unconventional Monetary Policies Across Euro Area Countries. European Economic Review, 101, 201-229. DOI: https://doi.org/10.1016/j.euroecorev.2017.10.007 Search in Google Scholar

CHOI, S., FURCERI, D., LOUNGANI, P., MISHRA, S., POPLAWSKI-RIBEIRO, M. (2018). Oil prices and inflation dynamics: Evidence from advanced and developing economies. Journal of International Money and Finance, 82, 71-96. DOI: https://doi.org/10.1016/j.jimonfin.2017.12.004 Search in Google Scholar

CICCARELLI, M., MOJON, B. (2010). Global inflation. The Review of Economics and Statistics, 92(3), 524–535. https://doi.org/10.1162/REST_a_00008 Search in Google Scholar

ČAKLOVICA, L., EFENDIC, A. (2020). Determinants of Inflation in Europe – A Dynamic Panel Analysis. Financial Internet Quarterly, 16(3), 51-79. https://doi.org/10.2478/fiqf-2020-0018 Search in Google Scholar

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. DOI: https://doi.org/10.1002/jae.932 Search in Google Scholar

FELDKIRCHER, M. (2015). A Global Macro Model for Emerging Europe. Journal of Comparative Economics, 43(3), 706-726. DOI: https://doi.org/10.1016/j.jce.2014.09.002 Search in Google Scholar

FLEMING, J. M. (1962). Domestic Financial Policies Under Fixed and Under Floating Exchange Rates. International Monetary Fund Staff Papers, 9(3), 369-379.10.2307/3866091 Search in Google Scholar

FURCERI, D., LOUNGANI, P., SIMON, J., WACHTER, S. M. (2016). Global food prices and domestic inflation: some cross-country evidence. Oxford Economic Papers, 68(3), 665-687. DOI: https://doi.org/10.1093/oep/gpw016 Search in Google Scholar

GLOBAN, T., ARČABIĆ, V., SORIĆ, P. (2016). Inflation in New EU Member States: A Domestically or Externally Driven Phenomenon?. Emerging Markets Finance and Trade, 52(1), 154-168. DOI: 10.1080/1540496X.2014.998547 DOI öffnenSearch in Google Scholar

GOLINELLI, R., ORSI, R. (2002). Modelling inflation in EU accession countries: the case of the Czech Republic, Hungary and Poland (No. 9). Ezoneplus working paper. Search in Google Scholar

GREENWOOD-NIMMO, M., NGUYEN, V. H. SHIN, Y. (2012). International linkages of the Korean economy: The global vector error-correcting macroeconometric modelling approach. Melbourne Institute Working Paper Series, Working Paper No. 18/12.10.2139/ssrn.2146400 Search in Google Scholar

HAŁKA, A., KOTŁOWSKI, J. (2017). Global or domestic? Which shocks drive inflation in European small open economies?. Emerging Markets Finance and Trade, 53(8), 1812-1835. DOI: 10.1080/1540496X.2016.1193001 DOI öffnenSearch in Google Scholar

HAMMERMANN, F. (2007). Nonmonetary determinants of inflation in Romania: A decomposition (No. 1322). Kiel Working Paper. Search in Google Scholar

HARBO, I., JOHANSEN, S., NIELSEN, B., RAHBEK, A. (1998). Asymptotic inference on cointegrating rank in partial systems. Journal of Business and Economic Statistics, 16 (4), 388-399. DOI: https://doi.org/10.1080/07350015.1998.10524779 Search in Google Scholar

JOHANSEN, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. DOI: https://doi.org/10.1016/0165-1889(88)90041-3 Search in Google Scholar

JOHANSEN, S. (1991). Estimation and hypothesis testing of cointegrating vectors in gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580. DOI: https://doi.org/10.2307/2938278 Search in Google Scholar

JOHANSEN, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press, Oxford.10.1093/0198774508.001.0001 Search in Google Scholar

JORDAN, T. (2016). The impact of international spillovers on Swiss inflation and the exchange rate. Journal of International Money and Finance, 68, 262–265. https://doi.org/10.1016/j.jimonfin.2016.02.005 Search in Google Scholar

JOVIČIĆ, G., KUNOVAC, D. (2017). What is driving inflation and GDP in a small European economy: the case of Croatia. Croatian National Bank Working Paper No. 49. Search in Google Scholar

KALIMERIS, D. (2011). The Main Determinants of Inflation of the EU and US Economy: A Panel Time Series Analysis. International Journal of Trade and Global Markets, 4(3), 279-89.10.1504/IJTGM.2011.041762 Search in Google Scholar

KRZNAR, I., KUNOVAC, D. (2010). Impact of External Shocks on Domestic Inflation and GDP. Croatian National Bank Working Papers No. 26. Search in Google Scholar

LOMBARDI, M. J., GALESI, A. (2009). External shocks and international inflation linkages: a global VAR analysis. ECB Working Paper No. 1062. DOI: http://dx.doi.org/10.2139/ssrn.141419210.2139/ssrn.1414192 Search in Google Scholar

MASSO, J., STAEHR, K. (2005). Inflation Dynamics and Nominal Adjustment in the Baltic States (University of Tartu- Faculty of Economics and Business Administartion Working Paper Series 35). Estonia: University of Tartu. Search in Google Scholar

MIHAILOV, A., RUMLER, F., SCHARLER, J. (2011). Inflation dynamics in the new EU member states: how relevant are external factors?. Review of International Economics, 19(1), 65-76. DOI: https://doi.org/10.1111/j.1467-9396.2010.00932.x Search in Google Scholar

MUNDELL, R. A. (1963). Capital Mobility and Stabilization Policy Under Fixed and Flexible Exchange Rates. Canadian Journal of Economics and Political Science, 29(4), 475-485. DOI: https://doi.org/10.2307/139336 Search in Google Scholar

NAGY, E. E., TENGELY, V. (2018). External and domestic drivers of inflation: The case study of Hungary. Russian Journal of Money and Finance, 77(3), 49–64. https://doi.org/10.31477/rjmf.201803.49 Search in Google Scholar

PARKER, M. (2017). Global Inflation: the Role of Food, Housing and Energy Prices (Working Paper Series 2024). Germany: European Central Bank. Search in Google Scholar

PESARAN, M. H., SCHUERMANN, T., TREUTLER, B. J. (2007). Global Business Cycles and Credit Risk. Ch. 9, in The risks of financial institutions, eds M. Carey and R. Stultz, University of Chicago Press., Chicago.10.7208/chicago/9780226092980.003.0010 Search in Google Scholar

PESARAN, M. H., SCHUERMANN, T., TREUTLER, B. J., WEINER, S. M. (2006). Macroeconomic Dynamics and Credit Risk: A Global Perspective. Journal of Money, Credit and Banking, 38(5), 1211-1262.10.1353/mcb.2006.0074 Search in Google Scholar

PESARAN, M. H., SCHUERMANN, T. WEINER, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics, 22 (2), 129-162. DOI: https://doi.org/10.1198/073500104000000019 Search in Google Scholar

PESARAN, M. H., SHIN, Y., SMITH, R. J. (2000). Structural analysis of vector-error correction models with exogenous I(1) variables. Journal of Econometrics, 97(2), 293-343. DOI: https://doi.org/10.1016/S0304-4076(99)00073-1 Search in Google Scholar

POP, R. E., MURĂRAȘU, B. (2018). The drivers of inflation in a small open European economy: The case of Romania. International Journal of Trade, Economics and Finance, 9, 84-87.10.18178/ijtef.2018.9.2.593 Search in Google Scholar

ROEGER, W. (2005). International oil price changes: impact of oil prices on growth and inflation in the EU/OECD. International Economics and Economic Policy, 2(1), 15-32. DOI: https://doi.org/10.1007/s10368-005-0027-z Search in Google Scholar

SMITH, L. V., GALESI, A. (2014). GVAR Toolbox 2.0. Retrieved from https://sites.google.com/site/gvarmodelling/gvar-toolbox. Search in Google Scholar

STAEHR, K. (2010). Inflation in the New EU Countries from Central and Eastern Europe: Theories and Panel Data Estimations (Bank of Estonia Working Papers wp 2010-06). Estonia: Bank of Estonia. Search in Google Scholar

SUN, Y., HEINZ, F. F., HO, G. (2013). Cross-Country Linkages in Europe: A Global VAR Analysis. IMF Working Paper, No. 13/194, 74.10.5089/9781484345474.001 Search in Google Scholar

SZAFRANEK, K., HAŁKA, A. (2019). Determinants of low inflation in an emerging, small open economy through the lens of aggregated and disaggregated approach. Emerging Markets Finance and Trade, 55(13), 3094-3111. DOI: 10.1080/1540496X.2018.1541793 DOI öffnenSearch in Google Scholar

WHITE, W. R. (2006). Is price stability enough? BIS Working Papers No 205.10.2139/ssrn.900074 Search in Google Scholar

WU, J. C., XIA, F. D. (2016). Measuring the macroeconomic impact of monetary policy at the zero lower bound. Journal of Money, Credit and Banking, 48(2-3), 253-291. DOI: https://doi.org/10.1111/jmcb.12300 Search in Google Scholar

ŽIVKOV, D., ĐURAŠKOVIĆ, J., MANIĆ, S. (2019). How do oil price changes affect inflation in Central and Eastern European countries? A wavelet-based Markov switching approach. Baltic Journal of Economics, 19(1), 84-104. DOI: https://doi.org/10.1080/1406099X.2018.1562011 Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo