Skip to main content

Analysis of GDP and Inflation Drivers in the European Union

  • Chapter
  • First Online:
Data-Centric Business and Applications

Abstract

This chapter describes different effects of inflation and GDP on the European Union countries pointing out the costs attendant on high and low rate of inflation. To improve the understanding of GDP and inflation differences there were applied statistical methods. They verified some of the big economic crises in the history. Nominal GDP developments yield similar results in both the shorter (1995–2015) and longer period (1967–2015), suggesting that only two common principal components are needed to explain a significant amount of variance in the data. Concerning the inflation, we have investigated the co-movements and the heterogeneity in inflation dynamics across the analyzed countries over two periods (1967–2015 and 1994–2015). The findings indicate that there are three substantial common principal components explaining 99.72% of the total variance in the consumer price indexes in the period from 1994–2015, that can be related to the common monetary policy in the euro area. Finally, the chapter employed multiple linear regression models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bec F, Mogliani M (2015) Nowcasting French GDP in real-time with surveys and “blocked” regressions: combining forecasts or pooling information? Int J Forecast 31(4):1021–1042. https://doi.org/10.1016/j.ijforecast.2014.11.006

    Article  Google Scholar 

  2. Bjørnland HC, Ravazzolo F, Thorsrud LA (2017) Forecasting GDP with global components: this time is different. Int J Forecast 33(1):153–173. https://doi.org/10.1016/j.ijforecast.2016.02.004

    Article  Google Scholar 

  3. Blanchard O, Johnson DR (2013) Macroeconomics. Pearson Education, Harlow

    Google Scholar 

  4. Colander DC, Gamber EN (2002) Macroeconomics. Pearson Education, New Jersey

    Google Scholar 

  5. Čaplánová A, Martincová M (2014) Inflation, unemployment and human capital from macroeconomic of view. Wolters Kluwer, Bratislava

    Google Scholar 

  6. Gerdesmeier D (2011) Price stability: why is it important to you? Frankfurt am Main: European Central Bank, 25–26. Available at: https://www.ecb.europa.eu/pub/pdf/other/price_stability_web_2011sk.pdf?3da7d27d233bb8ac51bdfc1a53e96822

  7. Gregus M, Kryvinska N (2015) Service orientation of enterprises—aspects, dimensions, technologies. Comenius University in Bratislava, ISBN: 9788022339780

    Google Scholar 

  8. Holmes MH (2016) Introduction to scientific computing and data analysis. Springer International Publishing Switzerland (2016)

    Google Scholar 

  9. Kaczor S, Kryvinska N (2013) It is all about services—fundamentals, drivers, and business models. The society of service science. J Serv Sci Res 5(2):125–154 (Springer)

    Article  Google Scholar 

  10. Kardaun OJWF (2005) Classical methods of statistics. Springer, Berlin

    MATH  Google Scholar 

  11. Kotlebová J, Sobek O (2007) Monetary policy: strategies, institutions and instruments. Iura Edition, Bratislava

    Google Scholar 

  12. Kryvinska N (2012) Building consistent formal specification for the service enterprise agility foundation. The society of service science. J Serv Sci Res 4(2):235–269 (Springer)

    Article  Google Scholar 

  13. Kryvinska N, Gregus M (2014) SOA and its business value in requirements, features, practices and methodologies. Comenius University in Bratislava, ISBN: 9788022337649

    Google Scholar 

  14. Mankiw NG (2004) Principles of economics. Thomson South-Western Mason, Ohio

    Google Scholar 

  15. Ray M, Anderson D (2011) Krugman’s economics for AP. Worth Publishers, New York

    Google Scholar 

  16. Rusnák M (2016) Nowcasting Czech GDP in real time. Econ Model 54(4):26–39. https://doi.org/10.1016/j.econmod.2015.12.010

    Article  Google Scholar 

  17. Schumacher C (2005) Forecasting German GDP using alternative factor models based on large datasets. Discussion Paper Series 1: Economic Studies No 24

    Google Scholar 

  18. Schumacher Ch, Breitung J (2008) Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int J Forecast 24(3):386–398

    Article  Google Scholar 

  19. Simionescu M, Dobeš K, Brezina I, Gaal A (2016) GDP rate in the European Union: simulations based on panel data models. J Int Stud 9(3):191–202. https://doi.org/10.14254/2071-8330.2016/9-3/15

    Article  Google Scholar 

  20. Soares MJ, Aguiar-Conraria L (2014) Inflation rate dynamics convergence within the Euro. In: Murgante B et al (eds) Computational science and its applications—ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8579. Springer, Cham

    Google Scholar 

  21. Sorić P (2018) Consumer confidence as a GDP determinant in New EU Member States: a view from a time-varying perspective. Empirica 45(2):261–282. https://doi.org/10.1007/s10663-016-9360-4

    Article  Google Scholar 

  22. Stoshikj M, Kryvinska N, Strauss C (2013) Project management as a service. In: The 15th international conference on information integration and web-based applications & services (iiWAS2013), 2–4 December 2013. ACM, Vienna, Austria, pp 220–228

    Google Scholar 

  23. Stoshikj M, Kryvinska N, Strauss C (2014) Efficient managing of complex programs with project management services. Global Journal of Flexible Systems Management, Special Issue on Flexible Complexity Management and Engineering by Innovative Services, vol 15, Issue 1. Springer, Berlin, pp 25–38

    Article  Google Scholar 

  24. Taş N, Hepsen A, Önder E (2013) Analyzing macroeconomic indicators of economic growth using panel data. J Finance Investment Anal 2(3):41–53. Available at SSRN: https://ssrn.com/abstract=2264388 or http://dx.doi.org/10.2139/ssrn.2264388

  25. WorldBank (2016) WorldBank data [Online] www.data.worldbank.org

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mária Bohdalová .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Klacsánová, K., Bohdalová, M. (2019). Analysis of GDP and Inflation Drivers in the European Union. In: Kryvinska, N., Greguš, M. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-94117-2_11

Download citation

Publish with us

Policies and ethics